Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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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+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:557850
+- loss:DenoisingAutoEncoderLoss
+base_model: google-bert/bert-base-cased
+widget:
+- source_sentence: A man his
+  sentences:
+  - A construction worker peeking out of a manhole while his coworker sits on the
+    sidewalk smiling.
+  - A man is jumping unto his filthy bed.
+  - A man is sitting in a chair and looking at something that he is holding.
+- source_sentence: A and a woman walking with a a
+  sentences:
+  - A man and a woman is walking with a dog across a beach
+  - A baby smiles while swinging in a blue infant swing.
+  - A man uses a projector to give a presentation.
+- source_sentence: blue
+  sentences:
+  - A baby wearing a bib makes a funny face at the camera.
+  - The man is wearing a blue shirt.
+  - There are three policemen on bikes making sure that the streets are cleared for
+    the president.
+- source_sentence: Two boys and
+  sentences:
+  - Two boys sitting and eating ice cream.
+  - A man with a hat, boots, and brown pants, is playing the violin outside in front
+    of a black structure.
+  - A man is a safety suit walking outside while another man in a dark suit walks
+    into a building.
+- source_sentence: A finds humorous that.
+  sentences:
+  - A older gentleman finds it humorous that he is getting his picture taken while
+    doing his laundry.
+  - A dark-skinned man smoking a cigarette near a green trashcan.
+  - A woman walks on a sidewalk wearing a white dress with a blue plaid pattern.
+datasets:
+- sentence-transformers/all-nli
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on google-bert/bert-base-cased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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-cased](https://huggingface.co/google-bert/bert-base-cased) <!-- at revision cd5ef92a9fb2f889e972770a36d4ed042daf221e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
+- **Language:** en
+<!-- - **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': False, 'pooling_mode_mean_tokens': True, '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("jinoooooooooo/bert-base-cased-nli-tsdae")
+# Run inference
+sentences = [
+    'A finds humorous that.',
+    'A older gentleman finds it humorous that he is getting his picture taken while doing his laundry.',
+    'A woman walks on a sidewalk wearing a white dress with a blue plaid pattern.',
+]
+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
+
+#### all-nli
+
+* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 557,850 training samples
+* Columns: <code>damaged</code> and <code>original</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | damaged                                                                          | original                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.45 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.49 tokens</li><li>max: 46 tokens</li></ul> |
+* Samples:
+  | damaged                      | original                                                                   |
+  |:-----------------------------|:---------------------------------------------------------------------------|
+  | <code>a horse jumps a</code> | <code>A person on a horse jumps over a broken down airplane.</code>        |
+  | <code>at</code>              | <code>Children smiling and waving at camera</code>                         |
+  | <code>boy jumping a.</code>  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> |
+* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)
+
+### Evaluation Dataset
+
+#### all-nli
+
+* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 6,584 evaluation samples
+* Columns: <code>damaged</code> and <code>original</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | damaged                                                                          | original                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 8.52 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.26 tokens</li><li>max: 69 tokens</li></ul> |
+* Samples:
+  | damaged                                                                                            | original                                                                                                                                                                       |
+  |:---------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Two while packages.</code>                                                                   | <code>Two women are embracing while holding to go packages.</code>                                                                                                             |
+  | <code>young children, with the number one with 2 are standing wooden in a bathroom in sink.</code> | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> |
+  | <code>A a during world city of</code>                                                              | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    |
+* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `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
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss | Validation Loss |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.016 | 100  | 7.3226        | 7.2198          |
+| 0.032 | 200  | 3.7141        | 6.3506          |
+| 0.048 | 300  | 3.0632        | 5.8854          |
+| 0.064 | 400  | 2.6549        | 5.7539          |
+| 0.08  | 500  | 2.5332        | 5.5007          |
+| 0.096 | 600  | 2.3137        | 5.5201          |
+| 0.112 | 700  | 2.2533        | 5.3476          |
+| 0.128 | 800  | 2.0654        | 5.3438          |
+| 0.144 | 900  | 1.9943        | 5.3552          |
+| 0.16  | 1000 | 1.9587        | 5.2709          |
+| 0.176 | 1100 | 1.8053        | 5.4117          |
+| 0.192 | 1200 | 1.7414        | 5.4315          |
+| 0.208 | 1300 | 1.6773        | 5.2983          |
+| 0.224 | 1400 | 1.6035        | 5.5064          |
+| 0.24  | 1500 | 1.5592        | 5.5167          |
+| 0.256 | 1600 | 1.5837        | 5.4428          |
+| 0.272 | 1700 | 1.469         | 5.5266          |
+| 0.288 | 1800 | 1.384         | 5.5159          |
+| 0.304 | 1900 | 1.3616        | 5.4305          |
+| 0.32  | 2000 | 1.3065        | 5.5076          |
+| 0.336 | 2100 | 1.3045        | 5.5460          |
+| 0.352 | 2200 | 1.3447        | 5.3051          |
+| 0.368 | 2300 | 1.3367        | 5.4867          |
+| 0.384 | 2400 | 1.148         | 5.6086          |
+| 0.4   | 2500 | 1.2229        | 5.5027          |
+| 0.416 | 2600 | 1.16          | 5.4446          |
+| 0.432 | 2700 | 1.1809        | 5.4059          |
+| 0.448 | 2800 | 1.2099        | 5.6255          |
+| 0.464 | 2900 | 1.1264        | 5.2683          |
+| 0.48  | 3000 | 1.1589        | 5.3651          |
+| 0.496 | 3100 | 1.0954        | 5.3109          |
+| 0.512 | 3200 | 1.0962        | 5.4071          |
+| 0.528 | 3300 | 1.1185        | 5.4022          |
+| 0.544 | 3400 | 1.0656        | 5.2648          |
+| 0.56  | 3500 | 1.0935        | 5.2185          |
+| 0.576 | 3600 | 1.0235        | 5.2950          |
+| 0.592 | 3700 | 1.0256        | 5.3534          |
+| 0.608 | 3800 | 0.9711        | 5.2015          |
+| 0.624 | 3900 | 0.9901        | 5.1011          |
+| 0.64  | 4000 | 0.9959        | 5.2055          |
+| 0.656 | 4100 | 1.0018        | 5.2456          |
+| 0.672 | 4200 | 0.9836        | 5.3166          |
+| 0.688 | 4300 | 1.0481        | 5.2324          |
+| 0.704 | 4400 | 0.9917        | 5.1831          |
+| 0.72  | 4500 | 0.9595        | 5.1268          |
+| 0.736 | 4600 | 1.0096        | 5.1112          |
+| 0.752 | 4700 | 0.9986        | 5.0724          |
+| 0.768 | 4800 | 0.9405        | 5.1163          |
+| 0.784 | 4900 | 0.9057        | 5.0673          |
+| 0.8   | 5000 | 0.9938        | 4.9926          |
+| 0.816 | 5100 | 0.9849        | 4.9733          |
+| 0.832 | 5200 | 0.8973        | 5.0531          |
+| 0.848 | 5300 | 0.924         | 5.0007          |
+| 0.864 | 5400 | 0.9516        | 5.0079          |
+| 0.88  | 5500 | 0.9637        | 4.9513          |
+| 0.896 | 5600 | 0.9232        | 5.0035          |
+| 0.912 | 5700 | 0.9518        | 4.9339          |
+| 0.928 | 5800 | 0.8939        | 4.9783          |
+| 0.944 | 5900 | 0.8752        | 4.9495          |
+| 0.96  | 6000 | 0.9187        | 4.9496          |
+| 0.976 | 6100 | 0.8987        | 4.9177          |
+| 0.992 | 6200 | 0.9034        | 4.9224          |
+
+
+### Framework Versions
+- Python: 3.11.9
+- Sentence Transformers: 3.4.0.dev0
+- Transformers: 4.47.0
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.1.0
+- Tokenizers: 0.21.0
+
+## 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-cased",
+  "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": "4.47.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0.dev0",
+    "transformers": "4.47.0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1f5effdbe814a1407bea11e19fbd6deb4b92cd6fb4645fdf06e12f12245a4fb
+size 433263448

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 +1,56 @@
+{
+  "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": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "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]"
+}