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--- |
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license: mit |
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base_model: numind/entity-recognition-general-sota-v1 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: entity-recognition-general-sota-v1-finetuned-ner-X |
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results: [] |
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datasets: |
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- Babelscape/multinerd |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: token-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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## Model description |
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# entity-recognition-general-sota-v1-finetuned-ner-X |
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This model is a fine-tuned version of [numind/entity-recognition-general-sota-v1](https://huggingface.co/numind/entity-recognition-general-sota-v1) on an Babelscape/MultiNerd dataset. |
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It achieves the following results on the validation set: |
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- Loss: 0.0228 |
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- Precision: 0.9472 |
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- Recall: 0.9621 |
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- F1: 0.9546 |
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- Accuracy: 0.9915 |
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## Training and evaluation data |
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The dataset if filtered on english language and sampled first 1M on train and 100k on validation. |
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further filtered with data containing atleast one tag from labels2ids mentioned below. |
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Train data - 110723 items |
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Validation data - 13126 items |
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Trained on below listed tags from the MultiNERD dataset. |
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labels2ids_B = { |
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"O": 0, |
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"B-PER": 1, |
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"I-PER": 2, |
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"B-ORG": 3, |
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"I-ORG": 4, |
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"B-LOC": 5, |
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"I-LOC": 6, |
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"B-ANIM": 7, |
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"I-ANIM": 8, |
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"B-DIS": 9, |
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"I-DIS": 10 |
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} |
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## Training procedure |
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HF Trainer module |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 35 |
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- eval_batch_size: 35 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training & Test set evaluation results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0214 | 1.0 | 3164 | 0.0228 | 0.9472 | 0.9621 | 0.9546 | 0.9915 | |
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Test set Evaluation results: |
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{ |
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'eval_loss': 0.017866812646389008, |
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'eval_precision': 0.9557654500384648, |
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'eval_recall': 0.9739558381603589, |
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'eval_accuracy': 0.9931328078645237, |
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'eval_runtime': 109.6919, |
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'eval_samples_per_second': 269.045, |
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'eval_steps_per_second': 33.631 |
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} |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
