Initial commit: fine-tuned BGE-small on custom Q&A data

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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

@@ -0,0 +1,461 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:100231
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-small-en-v1.5
+widget:
+- source_sentence: 'Represent this sentence for searching relevant passages: where
+    do the chances live on raising hope'
+  sentences:
+  - Raising Hope James "Jimmy" Chance is a 23-year old, living in the surreal fictional
+    town of Natesville, who impregnates a serial killer during a one-night stand.
+    Earning custody of his daughter, Hope, after the mother is sentenced to death,
+    Jimmy relies on his oddball but well-intentioned family for support in raising
+    the child.
+  - Quadripoint A quadripoint is a point on the Earth that touches the border of four
+    distinct territories.[1][2] The term has never been in common use—it may not have
+    been used before 1964 when it was possibly invented by the Office of the Geographer
+    of the United States Department of State.[3][n 1] The word does not appear in
+    the Oxford English Dictionary or Merriam-Webster Online dictionary, but it does
+    appear in the Encyclopædia Britannica,[4] as well as in the World Factbook articles
+    on Botswana, Namibia, Zambia, and Zimbabwe, dating as far back as 1990.[5]
+  - Show Me the Way to Go Home The song was recorded by several artists in the 1920s,
+    including radio personalities The Happiness Boys,[2] Vincent Lopez and his Orchestra,[2]
+    and the California Ramblers.[3] Throughout the twentieth into the twenty-first
+    century it has been recorded by numerous artists.
+- source_sentence: 'Represent this sentence for searching relevant passages: who wrote
+    the book of john in the bible'
+  sentences:
+  - Gospel of John Although the Gospel of John is anonymous,[1] Christian tradition
+    historically has attributed it to John the Apostle, son of Zebedee and one of
+    Jesus' Twelve Apostles. The gospel is so closely related in style and content
+    to the three surviving Johannine epistles that commentators treat the four books,[2]
+    along with the Book of Revelation, as a single corpus of Johannine literature,
+    albeit not necessarily written by the same author.[Notes 1]
+  - Levi Strauss & Co. Levi Strauss & Co. /ˌliːvaɪ ˈstraʊs/ is a privately held[5]
+    American clothing company known worldwide for its Levi's /ˌliːvaɪz/ brand of denim
+    jeans. It was founded in May 1853[6] when German immigrant Levi Strauss came from
+    Buttenheim, Bavaria, to San Francisco, California to open a west coast branch
+    of his brothers' New York dry goods business.[7] The company's corporate headquarters
+    is located in the Levi's Plaza in San Francisco.[8]
+  - Saturday Night Fever Tony's friends come to the car along with an intoxicated
+    Annette. Joey says she has agreed to have sex with everyone. Tony tries to lead
+    her away, but is subdued by Double J and Joey, and sullenly leaves with the group
+    in the car. Double J and Joey rape Annette. Bobby C. pulls the car over on the
+    Verrazano-Narrows Bridge for their usual cable-climbing antics. Instead of abstaining
+    as usual, Bobby performs stunts more recklessly than the rest of the gang. Realizing
+    that he is acting recklessly, Tony tries to get him to come down. Bobby's strong
+    sense of despair, the situation with Pauline, and Tony's broken promise to call
+    him earlier that day all lead to a suicidal tirade about Tony's lack of caring
+    before Bobby slips and falls to his death in the water below.
+- source_sentence: 'Represent this sentence for searching relevant passages: what
+    type of habitat do sea turtles live in'
+  sentences:
+  - Turbidity Governments have set standards on the allowable turbidity in drinking
+    water. In the United States, systems that use conventional or direct filtration
+    methods turbidity cannot be higher than 1.0 nephelometric turbidity units (NTU)
+    at the plant outlet and all samples for turbidity must be less than or equal to
+    0.3 NTU for at least 95 percent of the samples in any month. Systems that use
+    filtration other than the conventional or direct filtration must follow state
+    limits, which must include turbidity at no time exceeding 5 NTU. Many drinking
+    water utilities strive to achieve levels as low as 0.1 NTU.[11] The European standards
+    for turbidity state that it must be no more than 4 NTU.[12] The World Health Organization,
+    establishes that the turbidity of drinking water should not be more than 5 NTU,
+    and should ideally be below 1 NTU.[13]
+  - 'List of 1924 Winter Olympics medal winners Finnish speed skater Clas Thunberg
+    topped the medal count with five medals: three golds, one silver, and one bronze.
+    One of his competitors, Roald Larsen of Norway, also won five medals, with two
+    silver and three bronze medal-winning performances.[3] The first gold medalist
+    at these Games—and therefore the first gold medalist in Winter Olympic history—was
+    American speed skater Charles Jewtraw. Only one medal change took place after
+    the Games: in the ski jump competition, a marking error deprived American athlete
+    Anders Haugen of a bronze medal. Haugen pursued an appeal to the IOC many years
+    after the fact; he was awarded the medal after a 1974 decision in his favor.[1]'
+  - Sea turtle Sea turtles are generally found in the waters over continental shelves.
+    During the first three to five years of life, sea turtles spend most of their
+    time in the pelagic zone floating in seaweed mats. Green sea turtles in particular
+    are often found in Sargassum mats, in which they find shelter and food.[14] Once
+    the sea turtle has reached adulthood it moves closer to the shore.[15] Females
+    will come ashore to lay their eggs on sandy beaches during the nesting season.[16]
+- source_sentence: 'Represent this sentence for searching relevant passages: what
+    triggers the release of calcium from the sarcoplasmic reticulum'
+  sentences:
+  - Pretty Little Liars (season 7) The season consisted of 20 episodes, in which ten
+    episodes aired in the summer of 2016, with the remaining ten episodes aired from
+    April 2017.[2][3][4] The season's premiere aired on June 21, 2016, on Freeform.[5]
+    Production and filming began in the end of March 2016, which was confirmed by
+    showrunner I. Marlene King.[6] The season premiere was written by I. Marlene King
+    and directed by Ron Lagomarsino.[7] King revealed the title of the premiere on
+    Twitter on March 17, 2016.[8] On August 29, 2016, it was confirmed that this would
+    be the final season of the series.[9]
+  - Wentworth (TV series) A seventh season was commissioned in April 2018, before
+    the sixth-season premiere, with filming commencing the following week and a premiere
+    set for 2019.
+  - Sarcoplasmic reticulum Calcium ion release from the SR, occurs in the junctional
+    SR/terminal cisternae through a ryanodine receptor (RyR) and is known as a calcium
+    spark.[10] There are three types of ryanodine receptor, RyR1 (in skeletal muscle),
+    RyR2 (in cardiac muscle) and RyR3 (in the brain).[11] Calcium release through
+    ryanodine receptors in the SR is triggered differently in different muscles. In
+    cardiac and smooth muscle an electrical impulse (action potential) triggers calcium
+    ions to enter the cell through an L-type calcium channel located in the cell membrane
+    (smooth muscle) or T-tubule membrane (cardiac muscle). These calcium ions bind
+    to and activate the RyR, producing a larger increase in intracellular calcium.
+    In skeletal muscle, however, the L-type calcium channel is bound to the RyR. Therefore
+    activation of the L-type calcium channel, via an action potential, activates the
+    RyR directly, causing calcium release (see calcium sparks for more details).[12]
+    Also, caffeine (found in coffee) can bind to and stimulate RyR. Caffeine works
+    by making the RyR more sensitive to either the action potential (skeletal muscle)
+    or calcium (cardiac or smooth muscle) therefore producing calcium sparks more
+    often (this can result in increased heart rate, which is why we feel more awake
+    after coffee).[13]
+- source_sentence: 'Represent this sentence for searching relevant passages: what
+    topic do all scientific questions have in common'
+  sentences:
+  - 'Jane Wyatt Wyatt portrayed Amanda Grayson, Spock''s mother and Ambassador Sarek''s
+    (Mark Lenard) wife, in the 1967 episode "Journey to Babel" of the original NBC
+    series, Star Trek, and the 1986 film Star Trek IV: The Voyage Home.[9] Wyatt was
+    once quoted as saying her fan mail for these two appearances in this role exceeded
+    that of Lost Horizon. In 1969, she made a guest appearance on Here Come the Brides,
+    but did not have any scenes with Mark Lenard, who was starring on the show as
+    sawmill owner Aaron Stemple.'
+  - Minnesota Vikings The Vikings played in Super Bowl XI, their third Super Bowl
+    (fourth overall) in four years, against the Oakland Raiders at the Rose Bowl in
+    Pasadena, California, on January 9, 1977. The Vikings, however, lost 32–14.[1]
+  - List of topics characterized as pseudoscience Criticism of pseudoscience, generally
+    by the scientific community or skeptical organizations, involves critiques of
+    the logical, methodological, or rhetorical bases of the topic in question.[1]
+    Though some of the listed topics continue to be investigated scientifically, others
+    were only subject to scientific research in the past, and today are considered
+    refuted but resurrected in a pseudoscientific fashion. Other ideas presented here
+    are entirely non-scientific, but have in one way or another infringed on scientific
+    domains or practices.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 dimensions
+- **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': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (2): Normalize()
+)
+```
+
+## 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("srinivasanAI/bge-small-my-qna-model")
+# Run inference
+sentences = [
+    'Represent this sentence for searching relevant passages: what topic do all scientific questions have in common',
+    'List of topics characterized as pseudoscience Criticism of pseudoscience, generally by the scientific community or skeptical organizations, involves critiques of the logical, methodological, or rhetorical bases of the topic in question.[1] Though some of the listed topics continue to be investigated scientifically, others were only subject to scientific research in the past, and today are considered refuted but resurrected in a pseudoscientific fashion. Other ideas presented here are entirely non-scientific, but have in one way or another infringed on scientific domains or practices.',
+    'Jane Wyatt Wyatt portrayed Amanda Grayson, Spock\'s mother and Ambassador Sarek\'s (Mark Lenard) wife, in the 1967 episode "Journey to Babel" of the original NBC series, Star Trek, and the 1986 film Star Trek IV: The Voyage Home.[9] Wyatt was once quoted as saying her fan mail for these two appearances in this role exceeded that of Lost Horizon. In 1969, she made a guest appearance on Here Come the Brides, but did not have any scenes with Mark Lenard, who was starring on the show as sawmill owner Aaron Stemple.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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: 100,231 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: 18 tokens</li><li>mean: 19.69 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 139.68 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Represent this sentence for searching relevant passages: where did strangers prey at night take place</code>                                      | <code>The Strangers: Prey at Night In a secluded trailer park in Salem, Arkansas, the three masked killers, The Walker family — Dollface, Pin Up Girl, and the Man in the Mask — arrive. Dollface kills a female occupant and then lies down in bed next to the woman's sleeping husband.</code>                                                                                                                                                                                                                                                                                                                                                                           |
+  | <code>Represent this sentence for searching relevant passages: what is the average height of the highest peaks in the drakensberg mountain range</code> | <code>Drakensberg During the past 20 million years, further massive upliftment, especially in the East, has taken place in Southern Africa. As a result, most of the plateau lies above 1,000 m (3,300 ft) despite the extensive erosion. The plateau is tilted such that its highest point is in the east, and it slopes gently downwards towards the west and south. The elevation of the edge of the eastern escarpments is typically in excess of 2,000 m (6,600 ft). It reaches its highest point (over 3,000 m (9,800 ft)) where the escarpment forms part of the international border between Lesotho and the South African province of KwaZulu-Natal.[5][8]</code> |
+  | <code>Represent this sentence for searching relevant passages: name the two epics of india which are woven around with legends</code>                   | <code>Indian epic poetry Indian epic poetry is the epic poetry written in the Indian subcontinent, traditionally called Kavya (or Kāvya; Sanskrit: काव्य, IAST: kāvyá). The Ramayana and the Mahabharata, which were originally composed in Sanskrit and later translated into many other Indian languages, and The Five Great Epics of Tamil Literature and Sangam literature are some of the oldest surviving epic poems ever written.[1]</code>                                                                                                                                                                                                                         |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 1
+- `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`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 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`: 1
+- `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`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `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
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.1596 | 500  | 0.0556        |
+| 0.3192 | 1000 | 0.0245        |
+| 0.4788 | 1500 | 0.0236        |
+| 0.6384 | 2000 | 0.0179        |
+| 0.7980 | 2500 | 0.0202        |
+| 0.9575 | 3000 | 0.0184        |
+
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 4.1.0
+- Transformers: 4.53.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.9.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.2
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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 +1,30 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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": "4.53.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.53.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "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:d4838de2b2870ef5f6a1c2872d9aaf915484a289608042c7376e8ae8409a355e
+size 133462128

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "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_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}