Upload fine-tuned model

1_Pooling/config.json

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

@@ -0,0 +1,440 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:5000
+- loss:CosineSimilarityLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: Machine Learning Engineer. We are looking for a Machine Learning
+    Engineer to join our growing team and work on exciting projects.. Energy officer
+    later analysis.; Represent doctor must amount first new.; Standard store herself
+    buy.; Attorney later road drive high could new.; Public near program language..
+    Docker; Azure; Linux; JavaScript; React. SQL; DevOps; Linux; Python; TensorFlow;
+    C#
+  sentences:
+  - Backend Developer. Baby consider fall go. Year role financial firm physical prepare
+    wear financial. Mission training research me mouth home partner.. Article bad
+    style.; On see attorney traditional price reflect tough.; Pay training I.; President
+    the these.; Mouth close debate world nor sport security.. Docker; C#; Flask; NoSQL;
+    Django; Node.js; Cybersecurity. Operation low rich drive.; Receive middle likely.
+  - Cybersecurity Analyst. What light amount modern security receive. Build book street
+    challenge.. Light choice TV.; Beat piece usually day bar cost.; True government
+    sea training door.; Popular who also situation step.. React; TypeScript; NoSQL;
+    Deep Learning; C#; TensorFlow; Django. Hand behavior market religious four might
+    size.; Middle central knowledge fast rise all really.; Become fight argue.; Able
+    hundred force response.
+  - AI Researcher. Rather well administration police seat stand. Red produce yeah
+    site run fly purpose face.. Yourself address might expect his budget bill.; Later
+    top focus guess occur hour.; Have turn quickly help well its.; However research
+    visit.; Commercial building especially capital system each.. Python; Machine Learning;
+    Terraform; Deep Learning; Java; Cybersecurity; Linux. At exactly story letter
+    dream.; Paper experience control author like president girl education.; Education
+    fund hear side mother.; Who then more start various draw along.
+- source_sentence: Full Stack Developer. We are looking for a Full Stack Developer
+    to join our growing team and work on exciting projects.. Threat store center scene
+    country can quite.; Campaign today degree.; Data when risk citizen common.; Current
+    few environment social about page.. Penetration Testing; Java; Node.js; Docker;
+    JavaScript. SQL; AWS; CI/CD; JavaScript; Machine Learning; C#
+  sentences:
+  - Data Scientist. Protect usually song treat front he. Thought style successful
+    suddenly role voice also. When federal hear eat investment.. Campaign environmental
+    none federal.; These poor conference cause capital.; Start rule third ok.; Network
+    age job charge benefit various.; Go almost cost great.. DevOps; React; Cybersecurity;
+    SQL; Linux; Penetration Testing; Docker. Rise it interest try else attorney.;
+    Always everybody fight actually.; Nearly west score go.; Its if less system during.
+  - Frontend Developer. Student knowledge catch trip specific structure activity be.
+    Listen reveal member.. Nature radio serve into.; Speak old side green second travel
+    clear.; Family instead chance entire despite site.; Approach form wonder wrong
+    billion four blood source.. Flask; NoSQL; Azure; Cybersecurity; Node.js; Java;
+    C#. Recently how hot.; Push yourself step word they.; Forward per difficult chance
+    general ten.
+  - AI Researcher. Start ball civil set although. Or environmental place boy because
+    chance.. Within value ahead.; Class democratic candidate arm.; Region represent
+    great note nothing recently low.; Way live according follow walk doctor loss..
+    Azure; TensorFlow; JavaScript; Machine Learning; CI/CD; Kubernetes; Terraform.
+    Here other next over down seem yourself model.; Discover natural generation traditional
+    suddenly management.; Discuss food majority professor.
+- source_sentence: AI Researcher. We are looking for a AI Researcher to join our growing
+    team and work on exciting projects.. Improve hard street ask anyone accept history.;
+    Heavy a through old nothing various.; Fight clearly safe available similar hot.;
+    Movie body accept society heavy six.; Note close bad detail cell.. NoSQL; Azure;
+    Terraform; Flask; Django. Deep Learning; NoSQL; Terraform; Python; CI/CD; Flask
+  sentences:
+  - Frontend Developer. Model purpose most maintain price guess Republican. Manager
+    sure stuff beyond win. Wall type process.. Pattern million task so approach simple.;
+    Letter as tell tough price.; Tree ahead person building report likely see have.;
+    Thought name current hair avoid.. TensorFlow; Azure; DevOps; Machine Learning;
+    C#; React; NoSQL. Possible say son sister.; Nothing good later pressure board
+    stand.; Fly beat green picture stage.; Look sell same off else nature second.
+  - IT Project Manager. South although pass final number pick while. Others run contain
+    book. Bag single mission try true.. Power water determine go step common.; Student
+    people mission author stay.; Cup here father age age food.. React; Flask; Terraform;
+    DevOps; SQL; Docker; NoSQL. Marriage free security his before wear concern.; Future
+    great east use.; Senior plan require bit court often.
+  - Mobile App Developer. Until player time big design ten. Out billion money follow
+    bill so technology. Thousand north particularly difficult. Check social into decade
+    thing minute ahead.. Send memory ago full director although morning.; Relationship
+    sign front actually forget personal cold name.; Near debate notice their.. SQL;
+    AWS; C#; Node.js; Cybersecurity; Machine Learning; React. Produce fly sea.; Middle
+    race risk.; Land foot often action brother dinner.; Sign administration use book
+    section memory tree.
+- source_sentence: Machine Learning Engineer. We are looking for a Machine Learning
+    Engineer to join our growing team and work on exciting projects.. Talk serious
+    or mouth night measure.; Article ahead capital no development.; Do minute chance
+    employee.; Account impact product land never military main show.. Cybersecurity;
+    Terraform; Deep Learning; Python; Linux. Azure; Django; Docker; NoSQL; TypeScript;
+    SQL
+  sentences:
+  - Data Scientist. Next smile gun course six. Performance month bar let expect everything.
+    Whom great heart college people million computer.. Probably we determine information
+    century.; Step heavy animal notice foot police.; True soldier one business car..
+    AWS; JavaScript; Machine Learning; React; CI/CD; Linux; Cybersecurity. Feel field
+    behind matter hair.; Tonight give Mrs organization.
+  - Data Scientist. Among easy indicate statement. Sit natural change strategy start
+    party.. Do hand star modern.; Eat hear for will picture hotel.; Build parent true
+    discover carry involve exactly.. Python; TypeScript; Docker; NoSQL; C#; Linux;
+    SQL. Sound former during way suffer bag want.; History it school look.; Phone
+    into notice piece wait show.
+  - IT Project Manager. Least trade these voice. Choose letter than. Do model effort
+    they not.. Reflect development forward hand.; Investment fall what guess.; Green
+    new instead language board.. Kubernetes; TypeScript; Django; TensorFlow; AWS;
+    C#; Deep Learning. Lay tax group message work statement ago.; Can try heart city.;
+    Positive social increase throw seat share standard.; Front far prepare.
+- source_sentence: Software Engineer. We are looking for a Software Engineer to join
+    our growing team and work on exciting projects.. Suffer class note resource.;
+    Guess really character and right scientist behavior election.; Seat force cultural
+    arm while.; Single maintain from recently.; Not thing wife focus road.. CI/CD;
+    Terraform; DevOps; JavaScript; TypeScript. Docker; Java; Azure; Deep Learning;
+    AWS; Node.js
+  sentences:
+  - Full Stack Developer. Skin direction civil. Toward sure house stay sure if mouth
+    smile.. Range weight foreign.; Safe car at rest speech agency.; Her avoid her
+    heart three behind.; Deal goal send way power.. Azure; NoSQL; TypeScript; Java;
+    Kubernetes; Django; AWS. Environmental entire have charge state require artist.;
+    Among various instead our team.
+  - Software Engineer. Report ahead relate. Among employee that them.. Night continue
+    surface reduce instead education from.; None we forward notice miss wrong few.;
+    Business recently strategy else other recently environment.. Linux; NoSQL; Cybersecurity;
+    Machine Learning; Python; CI/CD; AWS. Social hot pay task commercial.; I throughout
+    participant sense.; Him station low happen available woman parent.; Measure recent
+    rock say city indeed allow value.
+  - Data Scientist. Standard defense clearly project.. Single always argue offer water
+    war.; Meeting certainly leader party heavy mind authority nearly.; Sister certain
+    any itself.; Paper top at area provide.. Cybersecurity; React; C#; TensorFlow;
+    Deep Learning; Penetration Testing; DevOps. Food safe wide key.; Word identify
+    cup life clear.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Software Engineer. We are looking for a Software Engineer to join our growing team and work on exciting projects.. Suffer class note resource.; Guess really character and right scientist behavior election.; Seat force cultural arm while.; Single maintain from recently.; Not thing wife focus road.. CI/CD; Terraform; DevOps; JavaScript; TypeScript. Docker; Java; Azure; Deep Learning; AWS; Node.js',
+    'Data Scientist. Standard defense clearly project.. Single always argue offer water war.; Meeting certainly leader party heavy mind authority nearly.; Sister certain any itself.; Paper top at area provide.. Cybersecurity; React; C#; TensorFlow; Deep Learning; Penetration Testing; DevOps. Food safe wide key.; Word identify cup life clear.',
+    'Software Engineer. Report ahead relate. Among employee that them.. Night continue surface reduce instead education from.; None we forward notice miss wrong few.; Business recently strategy else other recently environment.. Linux; NoSQL; Cybersecurity; Machine Learning; Python; CI/CD; AWS. Social hot pay task commercial.; I throughout participant sense.; Him station low happen available woman parent.; Measure recent rock say city indeed allow value.',
+]
+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: 5,000 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                          | sentence_1                                                                         | label                                                           |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             | float                                                           |
+  | details | <ul><li>min: 71 tokens</li><li>mean: 88.58 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 96.1 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.48</li><li>max: 0.83</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                 | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                       | label             |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>Machine Learning Engineer. We are looking for a Machine Learning Engineer to join our growing team and work on exciting projects.. Good kind capital special human.; Community great goal.; Reach discover wall blood black style after somebody.. Python; TensorFlow; React; CI/CD; AWS. DevOps; Django; Node.js; Azure; CI/CD; Kubernetes</code>                                   | <code>Mobile App Developer. At do American than partner sound. Plan decade industry deep establish wide whole.. All recognize edge southern.; Home hope house develop major there.; Crime push local present thus.. Node.js; Penetration Testing; TensorFlow; Java; C#; Deep Learning; NoSQL. Above people everything.; Eat game left past pull range.; Letter create must including.</code>                                                                     | <code>0.47</code> |
+  | <code>Mobile App Developer. We are looking for a Mobile App Developer to join our growing team and work on exciting projects.. Try hotel where catch reveal help.; Seat nor quality factor movie.; Good image realize respond possible.. Machine Learning; Terraform; JavaScript; Deep Learning; DevOps. Python; JavaScript; Cybersecurity; TensorFlow; Penetration Testing; DevOps</code> | <code>Mobile App Developer. Future large lead tree clear about building. Manage concern stuff shoulder.. Very star necessary military beautiful structure look.; Reveal something church particular instead special.; Than long series central.; Agent sister value.; Teacher production career more safe.. Penetration Testing; Node.js; SQL; TypeScript; Docker; React; Django. Ask song reveal.; Member top power certain pattern.; Trip away success.</code> | <code>0.42</code> |
+  | <code>Frontend Developer. We are looking for a Frontend Developer to join our growing team and work on exciting projects.. Per major government hotel population walk.; Suddenly artist century few research.; Exist to outside son onto member.. CI/CD; Python; Flask; Deep Learning; Java. SQL; Penetration Testing; AWS; Java; Linux; Node.js</code>                                    | <code>Software Engineer. Down which want debate. Situation establish find cold that. Take Republican over set people.. Understand event image suffer.; Kind go alone consumer develop tonight star.; Page radio former imagine evidence pick girl budget.. TypeScript; Cybersecurity; Machine Learning; Azure; NoSQL; SQL; React. End bed stand whatever challenge.; West moment act management can second between.</code>                                       | <code>0.46</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 10
+- `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
+- `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`: 10
+- `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}
+- `tp_size`: 0
+- `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
+- `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`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 1.5974 | 500  | 0.0052        |
+| 3.1949 | 1000 | 0.0039        |
+| 4.7923 | 1500 | 0.0033        |
+| 6.3898 | 2000 | 0.0029        |
+| 7.9872 | 2500 | 0.0026        |
+| 9.5847 | 3000 | 0.0023        |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.3",
+    "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 sha256:1f1da7461cd2e4b865d0f29ef7d24edee9784535b582e6a18f757c6a93c0da76
+size 90864192

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": 256,
+  "do_lower_case": false
+}

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,
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+  "pad_token": "[PAD]",
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+  "tokenizer_class": "BertTokenizer",
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+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}