Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: google-bert/bert-base-uncased
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:97043
+- loss:DenoisingAutoEncoderLoss
+widget:
+- source_sentence: ढचणच𑀟च𑀟
+  sentences:
+  - ढ𑀢ढल𑀢𑁣ब𑀪चध𑀫ण ढचणच𑀟च𑀟 𑀞नलच𑀠च𑀟च𑀤च𑀪पच𑀯
+  - ' णच 𑀪𑀢𑀞𑁦 𑀱च𑀟𑀟च𑀟 𑀠न𑀞च𑀠𑀢𑀟 𑀫च𑀪 𑀤न𑀱च 𑀭थ𑁢𑀰𑀯'
+  - ' च त𑀢𑀞𑀢𑀟 𑀠च𑀘चल𑀢𑀳च𑀪𑀠च𑀟च𑀤च𑀪पच𑀯'
+- source_sentence: त𑁣𑀠
+  sentences:
+  - ' 𑀲𑀪𑁦𑁦𑀣𑁣𑀠 𑀫𑁣न𑀳𑁦 पच ढच𑀢𑀱च 𑀳न𑀣च𑀟 𑀠चप𑀳चण𑀢 𑀠च𑀲𑀢 झच𑀳झच𑀟त𑀢 च प𑀳च𑀞च𑀟𑀢𑀟 ब𑀱च𑀠𑀟चप𑁣त𑀢𑀟 𑀣च𑀟𑀟𑀢णच
+    च 𑀳𑀫𑁦𑀞च𑀪च पच ठ𑀧𑀭ठ𑀯'
+  - 𑀖𑀖फ𑀮𑀦 𑁣𑀪𑁣𑀠𑁣 𑀝ठ𑀗𑀯
+  - त𑁣𑀠 𑀯
+- source_sentence: 𑀣च𑀟णच𑀟 𑀝𑀭थथ𑀬षठ𑀧𑀧ठ𑀮
+  sentences:
+  - ' 𑀣च𑀟णच𑀟 𑀝𑀭थथ𑀬षठ𑀧𑀧ठ𑀮 ध𑀪𑁣𑀲𑀯'
+  - 𑀳त𑀯
+  - ' 𑀳𑀫𑀢 ञच 𑀟𑁦 बच लच𑀲पच𑀟च𑀪 त𑁣ल𑀯'
+- source_sentence: 𑀠च𑀟च𑀤च𑀪पच𑀯
+  sentences:
+  - ' धच𑀪𑀞𑁦𑀪𑀦 लचनणच𑀟 ढ𑁣𑀳प𑁣𑀟𑀯'
+  - ब𑀪𑁦चपषधण𑀪च𑀠𑀢𑀣𑀯
+  - 𑀠च𑀟च𑀤च𑀪पच𑀯
+- source_sentence: 𑀫च𑀢𑀲𑀢 𑀳न𑀪𑁦𑀟𑀦 च
+  sentences:
+  - ' 𑀳𑀫त𑀫𑁦𑀪ढचप𑀢न𑀞 पच 𑀫च𑀢𑀲𑀢 ञच𑀦 𑀳न𑀪𑁦𑀟𑀦 च त𑀢𑀞𑀢𑀟 𑀭थ𑀖𑀗𑀯'
+  - 𑀯
+  - 𑀯
+---
+
+# SentenceTransformer based on google-bert/bert-base-uncased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("T-Blue/tsdae_pro_mbert")
+# Run inference
+sentences = [
+    '𑀫च𑀢𑀲𑀢 𑀳न𑀪𑁦𑀟𑀦 च',
+    ' 𑀳𑀫त𑀫𑁦𑀪ढचप𑀢न𑀞 पच 𑀫च𑀢𑀲𑀢 ञच𑀦 𑀳न𑀪𑁦𑀟𑀦 च त𑀢𑀞𑀢𑀟 𑀭थ𑀖𑀗𑀯',
+    '𑀯',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 97,043 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                       |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.12 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.06 tokens</li><li>max: 56 tokens</li></ul> |
+* Samples:
+  | sentence_0         | sentence_1              |
+  |:-------------------|:------------------------|
+  | <code>च𑀞𑀱च𑀢</code> | <code> च𑀞𑀱च𑀢 𑀭ठ𑀯</code> |
+  | <code>ठ𑀧𑀧𑁢𑀯</code> | <code> ठ𑀧𑀧𑁢𑀯</code>     |
+  | <code>𑁢𑀗𑀯</code>   | <code>𑁢𑀗𑀯</code>        |
+* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 5
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0824 | 500   | 1.1372        |
+| 0.1649 | 1000  | 0.8075        |
+| 0.2473 | 1500  | 0.7708        |
+| 0.3297 | 2000  | 0.7464        |
+| 0.4121 | 2500  | 0.7286        |
+| 0.4946 | 3000  | 0.7187        |
+| 0.5770 | 3500  | 0.7089        |
+| 0.6594 | 4000  | 0.6942        |
+| 0.7418 | 4500  | 0.7022        |
+| 0.8243 | 5000  | 0.6939        |
+| 0.9067 | 5500  | 0.6859        |
+| 0.9891 | 6000  | 0.6807        |
+| 1.0715 | 6500  | 0.6841        |
+| 1.1540 | 7000  | 0.6764        |
+| 1.2364 | 7500  | 0.6705        |
+| 1.3188 | 8000  | 0.6712        |
+| 1.4013 | 8500  | 0.6683        |
+| 1.4837 | 9000  | 0.6662        |
+| 1.5661 | 9500  | 0.6635        |
+| 1.6485 | 10000 | 0.655         |
+| 1.7310 | 10500 | 0.6667        |
+| 1.8134 | 11000 | 0.6533        |
+| 1.8958 | 11500 | 0.6564        |
+| 1.9782 | 12000 | 0.646         |
+| 2.0607 | 12500 | 0.6522        |
+| 2.1431 | 13000 | 0.6466        |
+| 2.2255 | 13500 | 0.6464        |
+| 2.3079 | 14000 | 0.647         |
+| 2.3904 | 14500 | 0.6408        |
+| 2.4728 | 15000 | 0.6415        |
+| 2.5552 | 15500 | 0.6397        |
+| 2.6377 | 16000 | 0.6303        |
+| 2.7201 | 16500 | 0.6465        |
+| 2.8025 | 17000 | 0.6287        |
+| 2.8849 | 17500 | 0.6358        |
+| 2.9674 | 18000 | 0.6247        |
+| 3.0498 | 18500 | 0.6318        |
+| 3.1322 | 19000 | 0.627         |
+| 3.2146 | 19500 | 0.6222        |
+| 3.2971 | 20000 | 0.6262        |
+| 3.3795 | 20500 | 0.6197        |
+| 3.4619 | 21000 | 0.6234        |
+| 3.5443 | 21500 | 0.6193        |
+| 3.6268 | 22000 | 0.6088        |
+| 3.7092 | 22500 | 0.624         |
+| 3.7916 | 23000 | 0.6089        |
+| 3.8741 | 23500 | 0.6184        |
+| 3.9565 | 24000 | 0.6047        |
+| 4.0389 | 24500 | 0.6066        |
+| 4.1213 | 25000 | 0.6082        |
+| 4.2038 | 25500 | 0.5999        |
+| 4.2862 | 26000 | 0.6046        |
+| 4.3686 | 26500 | 0.6038        |
+| 4.4510 | 27000 | 0.5978        |
+| 4.5335 | 27500 | 0.5948        |
+| 4.6159 | 28000 | 0.5887        |
+| 4.6983 | 28500 | 0.6031        |
+| 4.7807 | 29000 | 0.5823        |
+| 4.8632 | 29500 | 0.5953        |
+| 4.9456 | 30000 | 0.5793        |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.18.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### DenoisingAutoEncoderLoss
+```bibtex
+@inproceedings{wang-2021-TSDAE,
+    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
+    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", 
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+    month = nov,
+    year = "2021",
+    address = "Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    pages = "671--688",
+    url = "https://arxiv.org/abs/2104.06979",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.4",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2e713ebc4685c65245bff114ccb5fbf800ed9ab512a0d5f6ea051578aa186f7
+size 437951328

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}