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AhmedHassan19/model | https://huggingface.co/AhmedHassan19/model | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AhmedHassan19/model
### Model URL : https://huggingface.co/AhmedHassan19/model
### Model Description : No model card New: Create and edit this model card directly on the website! |
AhmedSSoliman/MarianCG-CoNaLa | https://huggingface.co/AhmedSSoliman/MarianCG-CoNaLa | This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset. MarianCG model and its implemetation with the code of training and the generated output is available at this repository:
https://github.com/AhmedSSoliman/MarianCG-NL-to-Code CoNaLa Dataset for Code Generation is available at
https://huggingface.co/datasets/AhmedSSoliman/CoNaLa This is the model is avialable on the huggingface hub https://huggingface.co/AhmedSSoliman/MarianCG-CoNaLa This model is available in spaces using gradio at: https://huggingface.co/spaces/AhmedSSoliman/MarianCG-CoNaLa Tasks: We now have a paper for this work and you can cite: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AhmedSSoliman/MarianCG-CoNaLa
### Model URL : https://huggingface.co/AhmedSSoliman/MarianCG-CoNaLa
### Model Description : This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset. MarianCG model and its implemetation with the code of training and the generated output is available at this repository:
https://github.com/AhmedSSoliman/MarianCG-NL-to-Code CoNaLa Dataset for Code Generation is available at
https://huggingface.co/datasets/AhmedSSoliman/CoNaLa This is the model is avialable on the huggingface hub https://huggingface.co/AhmedSSoliman/MarianCG-CoNaLa This model is available in spaces using gradio at: https://huggingface.co/spaces/AhmedSSoliman/MarianCG-CoNaLa Tasks: We now have a paper for this work and you can cite: |
Ahmedahmed/Wewe | https://huggingface.co/Ahmedahmed/Wewe | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahmedahmed/Wewe
### Model URL : https://huggingface.co/Ahmedahmed/Wewe
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ahren09/distilbert-base-uncased-finetuned-cola | https://huggingface.co/Ahren09/distilbert-base-uncased-finetuned-cola | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahren09/distilbert-base-uncased-finetuned-cola
### Model URL : https://huggingface.co/Ahren09/distilbert-base-uncased-finetuned-cola
### Model Description : No model card New: Create and edit this model card directly on the website! |
AiPorter/DialoGPT-small-Back_to_the_future | https://huggingface.co/AiPorter/DialoGPT-small-Back_to_the_future | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AiPorter/DialoGPT-small-Back_to_the_future
### Model URL : https://huggingface.co/AiPorter/DialoGPT-small-Back_to_the_future
### Model Description : |
Aibox/DialoGPT-small-rick | https://huggingface.co/Aibox/DialoGPT-small-rick | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aibox/DialoGPT-small-rick
### Model URL : https://huggingface.co/Aibox/DialoGPT-small-rick
### Model Description : |
Aidan8756/stephenKingModel | https://huggingface.co/Aidan8756/stephenKingModel | Trained on Stephen King's top 50 books as .txt files. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aidan8756/stephenKingModel
### Model URL : https://huggingface.co/Aidan8756/stephenKingModel
### Model Description : Trained on Stephen King's top 50 books as .txt files. |
AidenGO/KDXF_Bert4MaskedLM | https://huggingface.co/AidenGO/KDXF_Bert4MaskedLM | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AidenGO/KDXF_Bert4MaskedLM
### Model URL : https://huggingface.co/AidenGO/KDXF_Bert4MaskedLM
### Model Description : No model card New: Create and edit this model card directly on the website! |
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm | https://huggingface.co/AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm
### Model URL : https://huggingface.co/AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm
### Model Description : No model card New: Create and edit this model card directly on the website! |
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt | https://huggingface.co/AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set: Trained with this jupiter notebook In order to reduce the number of characters, the following letters have been replaced or removed: Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model. More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt
### Model URL : https://huggingface.co/AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set: Trained with this jupiter notebook In order to reduce the number of characters, the following letters have been replaced or removed: Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model. More information needed The following hyperparameters were used during training: |
AimB/konlpy_berttokenizer_helsinki | https://huggingface.co/AimB/konlpy_berttokenizer_helsinki | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AimB/konlpy_berttokenizer_helsinki
### Model URL : https://huggingface.co/AimB/konlpy_berttokenizer_helsinki
### Model Description : No model card New: Create and edit this model card directly on the website! |
AimB/mT5-en-kr-aihub-netflix | https://huggingface.co/AimB/mT5-en-kr-aihub-netflix | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AimB/mT5-en-kr-aihub-netflix
### Model URL : https://huggingface.co/AimB/mT5-en-kr-aihub-netflix
### Model Description : No model card New: Create and edit this model card directly on the website! |
AimB/mT5-en-kr-natural | https://huggingface.co/AimB/mT5-en-kr-natural | you can use this model with simpletransfomers. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AimB/mT5-en-kr-natural
### Model URL : https://huggingface.co/AimB/mT5-en-kr-natural
### Model Description : you can use this model with simpletransfomers. |
AimB/mT5-en-kr-opus | https://huggingface.co/AimB/mT5-en-kr-opus | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AimB/mT5-en-kr-opus
### Model URL : https://huggingface.co/AimB/mT5-en-kr-opus
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aimendo/Triage | https://huggingface.co/Aimendo/Triage | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aimendo/Triage
### Model URL : https://huggingface.co/Aimendo/Triage
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aimendo/autonlp-triage-35248482 | https://huggingface.co/Aimendo/autonlp-triage-35248482 | You can use cURL to access this model: Or Python API: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aimendo/autonlp-triage-35248482
### Model URL : https://huggingface.co/Aimendo/autonlp-triage-35248482
### Model Description : You can use cURL to access this model: Or Python API: |
Ajay191191/autonlp-Test-530014983 | https://huggingface.co/Ajay191191/autonlp-Test-530014983 | You can use cURL to access this model: Or Python API: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ajay191191/autonlp-Test-530014983
### Model URL : https://huggingface.co/Ajay191191/autonlp-Test-530014983
### Model Description : You can use cURL to access this model: Or Python API: |
Ajaykannan6/autonlp-manthan-16122692 | https://huggingface.co/Ajaykannan6/autonlp-manthan-16122692 | You can use cURL to access this model: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ajaykannan6/autonlp-manthan-16122692
### Model URL : https://huggingface.co/Ajaykannan6/autonlp-manthan-16122692
### Model Description : You can use cURL to access this model: |
Ajteks/Chatbot | https://huggingface.co/Ajteks/Chatbot | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ajteks/Chatbot
### Model URL : https://huggingface.co/Ajteks/Chatbot
### Model Description : No model card New: Create and edit this model card directly on the website! |
AkaiSnow/Rick_bot | https://huggingface.co/AkaiSnow/Rick_bot | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkaiSnow/Rick_bot
### Model URL : https://huggingface.co/AkaiSnow/Rick_bot
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akame/Vi | https://huggingface.co/Akame/Vi | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akame/Vi
### Model URL : https://huggingface.co/Akame/Vi
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akaramhuggingface/News | https://huggingface.co/Akaramhuggingface/News | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akaramhuggingface/News
### Model URL : https://huggingface.co/Akaramhuggingface/News
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akari/albert-base-v2-finetuned-squad | https://huggingface.co/Akari/albert-base-v2-finetuned-squad | This model is a fine-tuned version of albert-base-v2 on the squad_v2 dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akari/albert-base-v2-finetuned-squad
### Model URL : https://huggingface.co/Akari/albert-base-v2-finetuned-squad
### Model Description : This model is a fine-tuned version of albert-base-v2 on the squad_v2 dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Akash7897/bert-base-cased-wikitext2 | https://huggingface.co/Akash7897/bert-base-cased-wikitext2 | This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akash7897/bert-base-cased-wikitext2
### Model URL : https://huggingface.co/Akash7897/bert-base-cased-wikitext2
### Model Description : This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Akash7897/distilbert-base-uncased-finetuned-cola | https://huggingface.co/Akash7897/distilbert-base-uncased-finetuned-cola | This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akash7897/distilbert-base-uncased-finetuned-cola
### Model URL : https://huggingface.co/Akash7897/distilbert-base-uncased-finetuned-cola
### Model Description : This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Akash7897/distilbert-base-uncased-finetuned-sst2 | https://huggingface.co/Akash7897/distilbert-base-uncased-finetuned-sst2 | This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akash7897/distilbert-base-uncased-finetuned-sst2
### Model URL : https://huggingface.co/Akash7897/distilbert-base-uncased-finetuned-sst2
### Model Description : This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Akash7897/fill_mask_model | https://huggingface.co/Akash7897/fill_mask_model | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akash7897/fill_mask_model
### Model URL : https://huggingface.co/Akash7897/fill_mask_model
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akash7897/gpt2-wikitext2 | https://huggingface.co/Akash7897/gpt2-wikitext2 | This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akash7897/gpt2-wikitext2
### Model URL : https://huggingface.co/Akash7897/gpt2-wikitext2
### Model Description : This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Akash7897/my-newtokenizer | https://huggingface.co/Akash7897/my-newtokenizer | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akash7897/my-newtokenizer
### Model URL : https://huggingface.co/Akash7897/my-newtokenizer
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akash7897/test-clm | https://huggingface.co/Akash7897/test-clm | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akash7897/test-clm
### Model URL : https://huggingface.co/Akash7897/test-clm
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akashamba/distilbert-base-uncased-finetuned-ner | https://huggingface.co/Akashamba/distilbert-base-uncased-finetuned-ner | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashamba/distilbert-base-uncased-finetuned-ner
### Model URL : https://huggingface.co/Akashamba/distilbert-base-uncased-finetuned-ner
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akashpb13/Central_kurdish_xlsr | https://huggingface.co/Akashpb13/Central_kurdish_xlsr | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Central Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the train dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/Central_kurdish_xlsr
### Model URL : https://huggingface.co/Akashpb13/Central_kurdish_xlsr
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Central Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the train dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: |
Akashpb13/Galician_xlsr | https://huggingface.co/Akashpb13/Galician_xlsr | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Galician train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/Galician_xlsr
### Model URL : https://huggingface.co/Akashpb13/Galician_xlsr
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Galician train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: |
Akashpb13/Hausa_xlsr | https://huggingface.co/Akashpb13/Hausa_xlsr | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/Hausa_xlsr
### Model URL : https://huggingface.co/Akashpb13/Hausa_xlsr
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: |
Akashpb13/Kabyle_xlsr | https://huggingface.co/Akashpb13/Kabyle_xlsr | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset.
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/Kabyle_xlsr
### Model URL : https://huggingface.co/Akashpb13/Kabyle_xlsr
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset.
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: |
Akashpb13/Swahili_xlsr | https://huggingface.co/Akashpb13/Swahili_xlsr | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Hausa train.tsv and dev.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/Swahili_xlsr
### Model URL : https://huggingface.co/Akashpb13/Swahili_xlsr
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Hausa train.tsv and dev.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: |
Akashpb13/xlsr_hungarian_new | https://huggingface.co/Akashpb13/xlsr_hungarian_new | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice hungarian train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the train dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/xlsr_hungarian_new
### Model URL : https://huggingface.co/Akashpb13/xlsr_hungarian_new
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice hungarian train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the train dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: |
Akashpb13/xlsr_kurmanji_kurdish | https://huggingface.co/Akashpb13/xlsr_kurmanji_kurdish | This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Kurmanji Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/xlsr_kurmanji_kurdish
### Model URL : https://huggingface.co/Akashpb13/xlsr_kurmanji_kurdish
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): "facebook/wav2vec2-xls-r-300m" was finetuned. More information needed Training data -
Common voice Kurmanji Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 For creating the training dataset, all possible datasets were appended and 90-10 split was used. The following hyperparameters were used during training: |
Akashpb13/xlsr_maltese_wav2vec2 | https://huggingface.co/Akashpb13/xlsr_maltese_wav2vec2 | Fine-tuned facebook/wav2vec2-large-xlsr-53 in Maltese using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows: Test Result: 29.42 % | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akashpb13/xlsr_maltese_wav2vec2
### Model URL : https://huggingface.co/Akashpb13/xlsr_maltese_wav2vec2
### Model Description : Fine-tuned facebook/wav2vec2-large-xlsr-53 in Maltese using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows: Test Result: 29.42 % |
Akbarariza/Anjar | https://huggingface.co/Akbarariza/Anjar | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akbarariza/Anjar
### Model URL : https://huggingface.co/Akbarariza/Anjar
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akira-Yanagi/distilbert-base-uncased-finetuned-cola | https://huggingface.co/Akira-Yanagi/distilbert-base-uncased-finetuned-cola | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akira-Yanagi/distilbert-base-uncased-finetuned-cola
### Model URL : https://huggingface.co/Akira-Yanagi/distilbert-base-uncased-finetuned-cola
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akiva/Joke | https://huggingface.co/Akiva/Joke | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akiva/Joke
### Model URL : https://huggingface.co/Akiva/Joke
### Model Description : No model card New: Create and edit this model card directly on the website! |
Akjder/DialoGPT-small-harrypotter | https://huggingface.co/Akjder/DialoGPT-small-harrypotter | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akjder/DialoGPT-small-harrypotter
### Model URL : https://huggingface.co/Akjder/DialoGPT-small-harrypotter
### Model Description : |
Aklily/Lilys | https://huggingface.co/Aklily/Lilys | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aklily/Lilys
### Model URL : https://huggingface.co/Aklily/Lilys
### Model Description : No model card New: Create and edit this model card directly on the website! |
AkshatSurolia/BEiT-FaceMask-Finetuned | https://huggingface.co/AkshatSurolia/BEiT-FaceMask-Finetuned | BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshatSurolia/BEiT-FaceMask-Finetuned
### Model URL : https://huggingface.co/AkshatSurolia/BEiT-FaceMask-Finetuned
### Model Description : BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that. |
AkshatSurolia/ConvNeXt-FaceMask-Finetuned | https://huggingface.co/AkshatSurolia/ConvNeXt-FaceMask-Finetuned | ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshatSurolia/ConvNeXt-FaceMask-Finetuned
### Model URL : https://huggingface.co/AkshatSurolia/ConvNeXt-FaceMask-Finetuned
### Model Description : ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al. |
AkshatSurolia/DeiT-FaceMask-Finetuned | https://huggingface.co/AkshatSurolia/DeiT-FaceMask-Finetuned | Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshatSurolia/DeiT-FaceMask-Finetuned
### Model URL : https://huggingface.co/AkshatSurolia/DeiT-FaceMask-Finetuned
### Model Description : Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. |
AkshatSurolia/ICD-10-Code-Prediction | https://huggingface.co/AkshatSurolia/ICD-10-Code-Prediction | The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. Load the model via the transformers library: Run the model with clinical diagonosis text: Return the Top-5 predicted ICD-10 codes: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshatSurolia/ICD-10-Code-Prediction
### Model URL : https://huggingface.co/AkshatSurolia/ICD-10-Code-Prediction
### Model Description : The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. Load the model via the transformers library: Run the model with clinical diagonosis text: Return the Top-5 predicted ICD-10 codes: |
AkshatSurolia/ViT-FaceMask-Finetuned | https://huggingface.co/AkshatSurolia/ViT-FaceMask-Finetuned | Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al.
Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshatSurolia/ViT-FaceMask-Finetuned
### Model URL : https://huggingface.co/AkshatSurolia/ViT-FaceMask-Finetuned
### Model Description : Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al.
Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. |
AkshayDev/BERT_Fine_Tuning | https://huggingface.co/AkshayDev/BERT_Fine_Tuning | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshayDev/BERT_Fine_Tuning
### Model URL : https://huggingface.co/AkshayDev/BERT_Fine_Tuning
### Model Description : No model card New: Create and edit this model card directly on the website! |
AkshaySg/GrammarCorrection | https://huggingface.co/AkshaySg/GrammarCorrection | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshaySg/GrammarCorrection
### Model URL : https://huggingface.co/AkshaySg/GrammarCorrection
### Model Description : No model card New: Create and edit this model card directly on the website! |
AkshaySg/LanguageIdentification | https://huggingface.co/AkshaySg/LanguageIdentification | The model can classify a speech utterance according to the language spoken.
It covers following different languages (
English,
Indonesian,
Japanese,
Korean,
Thai,
Vietnamese,
Mandarin Chinese). | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshaySg/LanguageIdentification
### Model URL : https://huggingface.co/AkshaySg/LanguageIdentification
### Model Description : The model can classify a speech utterance according to the language spoken.
It covers following different languages (
English,
Indonesian,
Japanese,
Korean,
Thai,
Vietnamese,
Mandarin Chinese). |
AkshaySg/gramCorrection | https://huggingface.co/AkshaySg/gramCorrection | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshaySg/gramCorrection
### Model URL : https://huggingface.co/AkshaySg/gramCorrection
### Model Description : No model card New: Create and edit this model card directly on the website! |
AkshaySg/langid | https://huggingface.co/AkshaySg/langid | This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. The model can classify a speech utterance according to the language spoken.
It covers 107 different languages (
Abkhazian,
Afrikaans,
Amharic,
Arabic,
Assamese,
Azerbaijani,
Bashkir,
Belarusian,
Bulgarian,
Bengali,
Tibetan,
Breton,
Bosnian,
Catalan,
Cebuano,
Czech,
Welsh,
Danish,
German,
Greek,
English,
Esperanto,
Spanish,
Estonian,
Basque,
Persian,
Finnish,
Faroese,
French,
Galician,
Guarani,
Gujarati,
Manx,
Hausa,
Hawaiian,
Hindi,
Croatian,
Haitian,
Hungarian,
Armenian,
Interlingua,
Indonesian,
Icelandic,
Italian,
Hebrew,
Japanese,
Javanese,
Georgian,
Kazakh,
Central Khmer,
Kannada,
Korean,
Latin,
Luxembourgish,
Lingala,
Lao,
Lithuanian,
Latvian,
Malagasy,
Maori,
Macedonian,
Malayalam,
Mongolian,
Marathi,
Malay,
Maltese,
Burmese,
Nepali,
Dutch,
Norwegian Nynorsk,
Norwegian,
Occitan,
Panjabi,
Polish,
Pushto,
Portuguese,
Romanian,
Russian,
Sanskrit,
Scots,
Sindhi,
Sinhala,
Slovak,
Slovenian,
Shona,
Somali,
Albanian,
Serbian,
Sundanese,
Swedish,
Swahili,
Tamil,
Telugu,
Tajik,
Thai,
Turkmen,
Tagalog,
Turkish,
Tatar,
Ukrainian,
Urdu,
Uzbek,
Vietnamese,
Waray,
Yiddish,
Yoruba,
Mandarin Chinese). The model has two uses: The model is trained on automatically collected YouTube data. For more
information about the dataset, see here. Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: The model is trained on VoxLingua107. VoxLingua107 is a speech dataset for training spoken language identification models.
The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours.
The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. We used SpeechBrain to train the model.
Training recipe will be published soon. Error rate: 7% on the development dataset | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AkshaySg/langid
### Model URL : https://huggingface.co/AkshaySg/langid
### Model Description : This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. The model can classify a speech utterance according to the language spoken.
It covers 107 different languages (
Abkhazian,
Afrikaans,
Amharic,
Arabic,
Assamese,
Azerbaijani,
Bashkir,
Belarusian,
Bulgarian,
Bengali,
Tibetan,
Breton,
Bosnian,
Catalan,
Cebuano,
Czech,
Welsh,
Danish,
German,
Greek,
English,
Esperanto,
Spanish,
Estonian,
Basque,
Persian,
Finnish,
Faroese,
French,
Galician,
Guarani,
Gujarati,
Manx,
Hausa,
Hawaiian,
Hindi,
Croatian,
Haitian,
Hungarian,
Armenian,
Interlingua,
Indonesian,
Icelandic,
Italian,
Hebrew,
Japanese,
Javanese,
Georgian,
Kazakh,
Central Khmer,
Kannada,
Korean,
Latin,
Luxembourgish,
Lingala,
Lao,
Lithuanian,
Latvian,
Malagasy,
Maori,
Macedonian,
Malayalam,
Mongolian,
Marathi,
Malay,
Maltese,
Burmese,
Nepali,
Dutch,
Norwegian Nynorsk,
Norwegian,
Occitan,
Panjabi,
Polish,
Pushto,
Portuguese,
Romanian,
Russian,
Sanskrit,
Scots,
Sindhi,
Sinhala,
Slovak,
Slovenian,
Shona,
Somali,
Albanian,
Serbian,
Sundanese,
Swedish,
Swahili,
Tamil,
Telugu,
Tajik,
Thai,
Turkmen,
Tagalog,
Turkish,
Tatar,
Ukrainian,
Urdu,
Uzbek,
Vietnamese,
Waray,
Yiddish,
Yoruba,
Mandarin Chinese). The model has two uses: The model is trained on automatically collected YouTube data. For more
information about the dataset, see here. Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: The model is trained on VoxLingua107. VoxLingua107 is a speech dataset for training spoken language identification models.
The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours.
The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. We used SpeechBrain to train the model.
Training recipe will be published soon. Error rate: 7% on the development dataset |
Akuva2001/SocialGraph | https://huggingface.co/Akuva2001/SocialGraph | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Akuva2001/SocialGraph
### Model URL : https://huggingface.co/Akuva2001/SocialGraph
### Model Description : No model card New: Create and edit this model card directly on the website! |
Al/mymodel | https://huggingface.co/Al/mymodel | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Al/mymodel
### Model URL : https://huggingface.co/Al/mymodel
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlErysvi/Erys | https://huggingface.co/AlErysvi/Erys | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlErysvi/Erys
### Model URL : https://huggingface.co/AlErysvi/Erys
### Model Description : No model card New: Create and edit this model card directly on the website! |
Alaeddin/convbert-base-turkish-ner-cased | https://huggingface.co/Alaeddin/convbert-base-turkish-ner-cased | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Alaeddin/convbert-base-turkish-ner-cased
### Model URL : https://huggingface.co/Alaeddin/convbert-base-turkish-ner-cased
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlanDev/DallEMiniButBetter | https://huggingface.co/AlanDev/DallEMiniButBetter | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlanDev/DallEMiniButBetter
### Model URL : https://huggingface.co/AlanDev/DallEMiniButBetter
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlanDev/dall-e-better | https://huggingface.co/AlanDev/dall-e-better | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlanDev/dall-e-better
### Model URL : https://huggingface.co/AlanDev/dall-e-better
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlanDev/test | https://huggingface.co/AlanDev/test | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlanDev/test
### Model URL : https://huggingface.co/AlanDev/test
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlbertHSU/BertTEST | https://huggingface.co/AlbertHSU/BertTEST | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlbertHSU/BertTEST
### Model URL : https://huggingface.co/AlbertHSU/BertTEST
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlbertHSU/ChineseFoodBert | https://huggingface.co/AlbertHSU/ChineseFoodBert | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlbertHSU/ChineseFoodBert
### Model URL : https://huggingface.co/AlbertHSU/ChineseFoodBert
### Model Description : No model card New: Create and edit this model card directly on the website! |
Alberto15Romero/GptNeo | https://huggingface.co/Alberto15Romero/GptNeo | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Alberto15Romero/GptNeo
### Model URL : https://huggingface.co/Alberto15Romero/GptNeo
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlchemistDude/DialoGPT-medium-Gon | https://huggingface.co/AlchemistDude/DialoGPT-medium-Gon | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlchemistDude/DialoGPT-medium-Gon
### Model URL : https://huggingface.co/AlchemistDude/DialoGPT-medium-Gon
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ale/Alen | https://huggingface.co/Ale/Alen | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ale/Alen
### Model URL : https://huggingface.co/Ale/Alen
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleenbo/Arcane | https://huggingface.co/Aleenbo/Arcane | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleenbo/Arcane
### Model URL : https://huggingface.co/Aleenbo/Arcane
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar/bert-srb-base-cased-oscar | https://huggingface.co/Aleksandar/bert-srb-base-cased-oscar | This model is a fine-tuned version of on the None dataset. More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/bert-srb-base-cased-oscar
### Model URL : https://huggingface.co/Aleksandar/bert-srb-base-cased-oscar
### Model Description : This model is a fine-tuned version of on the None dataset. More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/bert-srb-ner-setimes-lr | https://huggingface.co/Aleksandar/bert-srb-ner-setimes-lr | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/bert-srb-ner-setimes-lr
### Model URL : https://huggingface.co/Aleksandar/bert-srb-ner-setimes-lr
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar/bert-srb-ner-setimes | https://huggingface.co/Aleksandar/bert-srb-ner-setimes | This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/bert-srb-ner-setimes
### Model URL : https://huggingface.co/Aleksandar/bert-srb-ner-setimes
### Model Description : This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/bert-srb-ner | https://huggingface.co/Aleksandar/bert-srb-ner | This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/bert-srb-ner
### Model URL : https://huggingface.co/Aleksandar/bert-srb-ner
### Model Description : This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/distilbert-srb-base-cased-oscar | https://huggingface.co/Aleksandar/distilbert-srb-base-cased-oscar | This model is a fine-tuned version of on the None dataset. More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/distilbert-srb-base-cased-oscar
### Model URL : https://huggingface.co/Aleksandar/distilbert-srb-base-cased-oscar
### Model Description : This model is a fine-tuned version of on the None dataset. More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/distilbert-srb-ner-setimes-lr | https://huggingface.co/Aleksandar/distilbert-srb-ner-setimes-lr | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/distilbert-srb-ner-setimes-lr
### Model URL : https://huggingface.co/Aleksandar/distilbert-srb-ner-setimes-lr
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar/distilbert-srb-ner-setimes | https://huggingface.co/Aleksandar/distilbert-srb-ner-setimes | This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/distilbert-srb-ner-setimes
### Model URL : https://huggingface.co/Aleksandar/distilbert-srb-ner-setimes
### Model Description : This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/distilbert-srb-ner | https://huggingface.co/Aleksandar/distilbert-srb-ner | This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/distilbert-srb-ner
### Model URL : https://huggingface.co/Aleksandar/distilbert-srb-ner
### Model Description : This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/electra-srb-ner-setimes-lr | https://huggingface.co/Aleksandar/electra-srb-ner-setimes-lr | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/electra-srb-ner-setimes-lr
### Model URL : https://huggingface.co/Aleksandar/electra-srb-ner-setimes-lr
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar/electra-srb-ner-setimes | https://huggingface.co/Aleksandar/electra-srb-ner-setimes | This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/electra-srb-ner-setimes
### Model URL : https://huggingface.co/Aleksandar/electra-srb-ner-setimes
### Model Description : This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/electra-srb-ner | https://huggingface.co/Aleksandar/electra-srb-ner | This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/electra-srb-ner
### Model URL : https://huggingface.co/Aleksandar/electra-srb-ner
### Model Description : This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar/electra-srb-oscar | https://huggingface.co/Aleksandar/electra-srb-oscar | This model is a fine-tuned version of on the None dataset. More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar/electra-srb-oscar
### Model URL : https://huggingface.co/Aleksandar/electra-srb-oscar
### Model Description : This model is a fine-tuned version of on the None dataset. More information needed More information needed More information needed The following hyperparameters were used during training: |
Aleksandar1932/distilgpt2-rock | https://huggingface.co/Aleksandar1932/distilgpt2-rock | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar1932/distilgpt2-rock
### Model URL : https://huggingface.co/Aleksandar1932/distilgpt2-rock
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar1932/gpt2-country | https://huggingface.co/Aleksandar1932/gpt2-country | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar1932/gpt2-country
### Model URL : https://huggingface.co/Aleksandar1932/gpt2-country
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar1932/gpt2-hip-hop | https://huggingface.co/Aleksandar1932/gpt2-hip-hop | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar1932/gpt2-hip-hop
### Model URL : https://huggingface.co/Aleksandar1932/gpt2-hip-hop
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar1932/gpt2-pop | https://huggingface.co/Aleksandar1932/gpt2-pop | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar1932/gpt2-pop
### Model URL : https://huggingface.co/Aleksandar1932/gpt2-pop
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar1932/gpt2-rock-124439808 | https://huggingface.co/Aleksandar1932/gpt2-rock-124439808 | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar1932/gpt2-rock-124439808
### Model URL : https://huggingface.co/Aleksandar1932/gpt2-rock-124439808
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar1932/gpt2-soul | https://huggingface.co/Aleksandar1932/gpt2-soul | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar1932/gpt2-soul
### Model URL : https://huggingface.co/Aleksandar1932/gpt2-soul
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandar1932/gpt2-spanish-classics | https://huggingface.co/Aleksandar1932/gpt2-spanish-classics | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandar1932/gpt2-spanish-classics
### Model URL : https://huggingface.co/Aleksandar1932/gpt2-spanish-classics
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandra/distilbert-base-uncased-finetuned-squad | https://huggingface.co/Aleksandra/distilbert-base-uncased-finetuned-squad | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandra/distilbert-base-uncased-finetuned-squad
### Model URL : https://huggingface.co/Aleksandra/distilbert-base-uncased-finetuned-squad
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aleksandra/herbert-base-cased-finetuned-squad | https://huggingface.co/Aleksandra/herbert-base-cased-finetuned-squad | This model is a fine-tuned version of allegro/herbert-base-cased on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aleksandra/herbert-base-cased-finetuned-squad
### Model URL : https://huggingface.co/Aleksandra/herbert-base-cased-finetuned-squad
### Model Description : This model is a fine-tuned version of allegro/herbert-base-cased on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
adorkin/xlm-roberta-en-ru-emoji | https://huggingface.co/adorkin/xlm-roberta-en-ru-emoji | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : adorkin/xlm-roberta-en-ru-emoji
### Model URL : https://huggingface.co/adorkin/xlm-roberta-en-ru-emoji
### Model Description : |
AlekseyKorshuk/bert | https://huggingface.co/AlekseyKorshuk/bert | This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlekseyKorshuk/bert
### Model URL : https://huggingface.co/AlekseyKorshuk/bert
### Model Description : This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
AlekseyKorshuk/comedy-scripts | https://huggingface.co/AlekseyKorshuk/comedy-scripts | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlekseyKorshuk/comedy-scripts
### Model URL : https://huggingface.co/AlekseyKorshuk/comedy-scripts
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlekseyKorshuk/horror-scripts | https://huggingface.co/AlekseyKorshuk/horror-scripts | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlekseyKorshuk/horror-scripts
### Model URL : https://huggingface.co/AlekseyKorshuk/horror-scripts
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlekseyKulnevich/Pegasus-HeaderGeneration | https://huggingface.co/AlekseyKulnevich/Pegasus-HeaderGeneration | Usage HuggingFace Transformers for header generation task Decoder configuration examples:Input text you can see here output: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlekseyKulnevich/Pegasus-HeaderGeneration
### Model URL : https://huggingface.co/AlekseyKulnevich/Pegasus-HeaderGeneration
### Model Description : Usage HuggingFace Transformers for header generation task Decoder configuration examples:Input text you can see here output: |
AlekseyKulnevich/Pegasus-QuestionGeneration | https://huggingface.co/AlekseyKulnevich/Pegasus-QuestionGeneration | Usage HuggingFace Transformers for question generation task Decoder configuration examples: Input text you can see here output: Also you can play with the following parameters in generate method:-top_k-top_p Meaning of parameters to generate text you can see here | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlekseyKulnevich/Pegasus-QuestionGeneration
### Model URL : https://huggingface.co/AlekseyKulnevich/Pegasus-QuestionGeneration
### Model Description : Usage HuggingFace Transformers for question generation task Decoder configuration examples: Input text you can see here output: Also you can play with the following parameters in generate method:-top_k-top_p Meaning of parameters to generate text you can see here |
AlekseyKulnevich/Pegasus-Summarization | https://huggingface.co/AlekseyKulnevich/Pegasus-Summarization | Usage HuggingFace Transformers for summarization task Decoder configuration examples:Input text you can see here output: output: Also you can play with the following parameters in generate method:-top_k-top_pMeaning of parameters to generate text you can see here | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlekseyKulnevich/Pegasus-Summarization
### Model URL : https://huggingface.co/AlekseyKulnevich/Pegasus-Summarization
### Model Description : Usage HuggingFace Transformers for summarization task Decoder configuration examples:Input text you can see here output: output: Also you can play with the following parameters in generate method:-top_k-top_pMeaning of parameters to generate text you can see here |
Alerosae/SocratesGPT-2 | https://huggingface.co/Alerosae/SocratesGPT-2 | This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Alerosae/SocratesGPT-2
### Model URL : https://huggingface.co/Alerosae/SocratesGPT-2
### Model Description : This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly! |
Alessandro/model_name | https://huggingface.co/Alessandro/model_name | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Alessandro/model_name
### Model URL : https://huggingface.co/Alessandro/model_name
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlexDemon/Alex | https://huggingface.co/AlexDemon/Alex | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlexDemon/Alex
### Model URL : https://huggingface.co/AlexDemon/Alex
### Model Description : No model card New: Create and edit this model card directly on the website! |
AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | https://huggingface.co/AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets SQuAD + SberQuAD SberQuAD original paper is here! Recommend to read! The results obtained are the following (SberQUaD): | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru
### Model URL : https://huggingface.co/AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru
### Model Description : Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets SQuAD + SberQuAD SberQuAD original paper is here! Recommend to read! The results obtained are the following (SberQUaD): |
AlexMaclean/sentence-compression-roberta | https://huggingface.co/AlexMaclean/sentence-compression-roberta | This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AlexMaclean/sentence-compression-roberta
### Model URL : https://huggingface.co/AlexMaclean/sentence-compression-roberta
### Model Description : This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |