Upload folder using huggingface_hub

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 +1,62 @@
+
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- autotrain
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: 'search_query: i love autotrain'
+  sentences:
+  - 'search_query: huggingface auto train'
+  - 'search_query: hugging face auto train'
+  - 'search_query: i love autotrain'
+pipeline_tag: sentence-similarity
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Sentence Transformers
+
+## Validation Metrics
+loss: 0.6764523983001709
+
+runtime: 40.1143
+
+samples_per_second: 49.858
+
+steps_per_second: 3.116
+
+: 3.0
+
+## 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 Hugging Face Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'search_query: autotrain',
+    'search_query: auto train',
+    'search_query: i love autotrain',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+```

checkpoint-3000/1_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
+}

checkpoint-3000/README.md

@@ -0,0 +1,541 @@
+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:8000
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: As a user, I want to reset my password via email so that I can
+    regain access.
+  sentences:
+  - 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
+    are saved and reflected in the user's profile.<br>3. Test the validation of profile
+    fields (e.g., email format).
+  - 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
+    are saved and reflected in the user's profile.<br>3. Test the validation of profile
+    fields (e.g., email format).
+  - 1. Verify that the password reset email is sent to the user's registered email
+    address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
+    password reset link to confirm it allows setting a new password.
+- source_sentence: As a user, I want to update my profile information so that my account
+    details are current.
+  sentences:
+  - 1. Ensure the user can access the 'Order History' page.<br>2. Verify that the
+    page displays previous orders correctly.<br>3. Test the ability to filter orders
+    by date or status.
+  - 1. Verify that the password reset email is sent to the user's registered email
+    address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
+    password reset link to confirm it allows setting a new password.
+  - 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
+    are saved and reflected in the user's profile.<br>3. Test the validation of profile
+    fields (e.g., email format).
+- source_sentence: As a customer, I want to receive notifications for order status
+    updates so that I stay informed.
+  sentences:
+  - 1. Check that notifications are sent for order status changes.<br>2. Verify that
+    notifications include accurate order details.<br>3. Test the notification settings
+    to ensure users can customize their preferences.
+  - 1. Verify that the password reset email is sent to the user's registered email
+    address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
+    password reset link to confirm it allows setting a new password.
+  - 1. Ensure the user can access the 'Order History' page.<br>2. Verify that the
+    page displays previous orders correctly.<br>3. Test the ability to filter orders
+    by date or status.
+- source_sentence: As a user, I want to reset my password via email so that I can
+    regain access.
+  sentences:
+  - 1. Ensure the user can access the 'Order History' page.<br>2. Verify that the
+    page displays previous orders correctly.<br>3. Test the ability to filter orders
+    by date or status.
+  - 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
+    are saved and reflected in the user's profile.<br>3. Test the validation of profile
+    fields (e.g., email format).
+  - 1. Verify that the password reset email is sent to the user's registered email
+    address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
+    password reset link to confirm it allows setting a new password.
+- source_sentence: As a user, I want to update my profile information so that my account
+    details are current.
+  sentences:
+  - 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
+    are saved and reflected in the user's profile.<br>3. Test the validation of profile
+    fields (e.g., email format).
+  - 1. Confirm that an admin can access the 'Add New User' form.<br>2. Verify that
+    the form allows entering user details and submitting.<br>3. Check that the new
+    user is added to the user list after submission.
+  - 1. Test the search functionality by entering a product name and verifying that
+    the results include the correct product.<br>2. Ensure the search returns results
+    quickly.<br>3. Verify that searching for non-existent products returns no results.
+---
+
+# 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 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 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': 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 = [
+    'As a user, I want to update my profile information so that my account details are current.',
+    "1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes are saved and reflected in the user's profile.<br>3. Test the validation of profile fields (e.g., email format).",
+    '1. Test the search functionality by entering a product name and verifying that the results include the correct product.<br>2. Ensure the search returns results quickly.<br>3. Verify that searching for non-existent products returns no results.',
+]
+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: 8,000 training samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | answer                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 21 tokens</li><li>mean: 22.0 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 51.98 tokens</li><li>max: 56 tokens</li></ul> |
+* Samples:
+  | query                                                                                               | answer                                                                                                                                                                                                                                                          |
+  |:----------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>As a user, I want to search for products by name so that I can find specific items.</code>    | <code>1. Test the search functionality by entering a product name and verifying that the results include the correct product.<br>2. Ensure the search returns results quickly.<br>3. Verify that searching for non-existent products returns no results.</code> |
+  | <code>As an admin, I want to add new users to the system so that I can manage user accounts.</code> | <code>1. Confirm that an admin can access the 'Add New User' form.<br>2. Verify that the form allows entering user details and submitting.<br>3. Check that the new user is added to the user list after submission.</code>                                     |
+  | <code>As a user, I want to search for products by name so that I can find specific items.</code>    | <code>1. Test the search functionality by entering a product name and verifying that the results include the correct product.<br>2. Ensure the search returns results quickly.<br>3. Verify that searching for non-existent products returns no results.</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"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 2,000 evaluation samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | answer                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 21 tokens</li><li>mean: 22.0 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 51.54 tokens</li><li>max: 56 tokens</li></ul> |
+* Samples:
+  | query                                                                                                         | answer                                                                                                                                                                                                                                      |
+  |:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>As a user, I want to reset my password via email so that I can regain access.</code>                    | <code>1. Verify that the password reset email is sent to the user's registered email address.<br>2. Ensure the email contains a password reset link.<br>3. Test the password reset link to confirm it allows setting a new password.</code> |
+  | <code>As a customer, I want to receive notifications for order status updates so that I stay informed.</code> | <code>1. Check that notifications are sent for order status changes.<br>2. Verify that notifications include accurate order details.<br>3. Test the notification settings to ensure users can customize their preferences.</code>           |
+  | <code>As a user, I want to view my order history so that I can track my previous purchases.</code>            | <code>1. Ensure the user can access the 'Order History' page.<br>2. Verify that the page displays previous orders correctly.<br>3. Test the ability to filter orders by date or status.</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
+
+- `eval_strategy`: epoch
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 3e-05
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `ddp_find_unused_parameters`: False
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `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`: 3e-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`: 3
+- `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`: True
+- `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`: False
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch | Step | Training Loss | loss   |
+|:-----:|:----:|:-------------:|:------:|
+| 0.025 | 25   | 0.6305        | -      |
+| 0.05  | 50   | 0.7491        | -      |
+| 0.075 | 75   | 0.6931        | -      |
+| 0.1   | 100  | 0.7093        | -      |
+| 0.125 | 125  | 0.631         | -      |
+| 0.15  | 150  | 0.6127        | -      |
+| 0.175 | 175  | 0.5963        | -      |
+| 0.2   | 200  | 0.6556        | -      |
+| 0.225 | 225  | 0.6831        | -      |
+| 0.25  | 250  | 0.6389        | -      |
+| 0.275 | 275  | 0.6913        | -      |
+| 0.3   | 300  | 0.6447        | -      |
+| 0.325 | 325  | 0.6993        | -      |
+| 0.35  | 350  | 0.6581        | -      |
+| 0.375 | 375  | 0.6695        | -      |
+| 0.4   | 400  | 0.7076        | -      |
+| 0.425 | 425  | 0.6301        | -      |
+| 0.45  | 450  | 0.6121        | -      |
+| 0.475 | 475  | 0.6439        | -      |
+| 0.5   | 500  | 0.6782        | -      |
+| 0.525 | 525  | 0.7025        | -      |
+| 0.55  | 550  | 0.7228        | -      |
+| 0.575 | 575  | 0.6065        | -      |
+| 0.6   | 600  | 0.6496        | -      |
+| 0.625 | 625  | 0.6816        | -      |
+| 0.65  | 650  | 0.6302        | -      |
+| 0.675 | 675  | 0.692         | -      |
+| 0.7   | 700  | 0.7533        | -      |
+| 0.725 | 725  | 0.6567        | -      |
+| 0.75  | 750  | 0.6472        | -      |
+| 0.775 | 775  | 0.6461        | -      |
+| 0.8   | 800  | 0.661         | -      |
+| 0.825 | 825  | 0.6897        | -      |
+| 0.85  | 850  | 0.6097        | -      |
+| 0.875 | 875  | 0.6284        | -      |
+| 0.9   | 900  | 0.5923        | -      |
+| 0.925 | 925  | 0.6642        | -      |
+| 0.95  | 950  | 0.6531        | -      |
+| 0.975 | 975  | 0.6705        | -      |
+| 1.0   | 1000 | 0.7137        | 0.6765 |
+| 1.025 | 1025 | 0.6601        | -      |
+| 1.05  | 1050 | 0.6739        | -      |
+| 1.075 | 1075 | 0.6487        | -      |
+| 1.1   | 1100 | 0.6864        | -      |
+| 1.125 | 1125 | 0.7744        | -      |
+| 1.15  | 1150 | 0.6698        | -      |
+| 1.175 | 1175 | 0.6421        | -      |
+| 1.2   | 1200 | 0.633         | -      |
+| 1.225 | 1225 | 0.678         | -      |
+| 1.25  | 1250 | 0.6264        | -      |
+| 1.275 | 1275 | 0.721         | -      |
+| 1.3   | 1300 | 0.6736        | -      |
+| 1.325 | 1325 | 0.5332        | -      |
+| 1.35  | 1350 | 0.6576        | -      |
+| 1.375 | 1375 | 0.6625        | -      |
+| 1.4   | 1400 | 0.7248        | -      |
+| 1.425 | 1425 | 0.6188        | -      |
+| 1.45  | 1450 | 0.6452        | -      |
+| 1.475 | 1475 | 0.7024        | -      |
+| 1.5   | 1500 | 0.7005        | -      |
+| 1.525 | 1525 | 0.6219        | -      |
+| 1.55  | 1550 | 0.6525        | -      |
+| 1.575 | 1575 | 0.6718        | -      |
+| 1.6   | 1600 | 0.6738        | -      |
+| 1.625 | 1625 | 0.6558        | -      |
+| 1.65  | 1650 | 0.6236        | -      |
+| 1.675 | 1675 | 0.7126        | -      |
+| 1.7   | 1700 | 0.6822        | -      |
+| 1.725 | 1725 | 0.6324        | -      |
+| 1.75  | 1750 | 0.7036        | -      |
+| 1.775 | 1775 | 0.6765        | -      |
+| 1.8   | 1800 | 0.654         | -      |
+| 1.825 | 1825 | 0.6923        | -      |
+| 1.85  | 1850 | 0.6976        | -      |
+| 1.875 | 1875 | 0.6904        | -      |
+| 1.9   | 1900 | 0.6307        | -      |
+| 1.925 | 1925 | 0.6437        | -      |
+| 1.95  | 1950 | 0.6333        | -      |
+| 1.975 | 1975 | 0.6214        | -      |
+| 2.0   | 2000 | 0.6424        | 0.6765 |
+| 2.025 | 2025 | 0.7041        | -      |
+| 2.05  | 2050 | 0.7112        | -      |
+| 2.075 | 2075 | 0.6696        | -      |
+| 2.1   | 2100 | 0.6739        | -      |
+| 2.125 | 2125 | 0.6315        | -      |
+| 2.15  | 2150 | 0.7649        | -      |
+| 2.175 | 2175 | 0.7079        | -      |
+| 2.2   | 2200 | 0.6549        | -      |
+| 2.225 | 2225 | 0.6548        | -      |
+| 2.25  | 2250 | 0.6647        | -      |
+| 2.275 | 2275 | 0.7568        | -      |
+| 2.3   | 2300 | 0.6659        | -      |
+| 2.325 | 2325 | 0.604         | -      |
+| 2.35  | 2350 | 0.6342        | -      |
+| 2.375 | 2375 | 0.6891        | -      |
+| 2.4   | 2400 | 0.6856        | -      |
+| 2.425 | 2425 | 0.6683        | -      |
+| 2.45  | 2450 | 0.678         | -      |
+| 2.475 | 2475 | 0.7102        | -      |
+| 2.5   | 2500 | 0.6606        | -      |
+| 2.525 | 2525 | 0.6634        | -      |
+| 2.55  | 2550 | 0.6443        | -      |
+| 2.575 | 2575 | 0.6122        | -      |
+| 2.6   | 2600 | 0.6926        | -      |
+| 2.625 | 2625 | 0.5957        | -      |
+| 2.65  | 2650 | 0.6933        | -      |
+| 2.675 | 2675 | 0.691         | -      |
+| 2.7   | 2700 | 0.7015        | -      |
+| 2.725 | 2725 | 0.7057        | -      |
+| 2.75  | 2750 | 0.6386        | -      |
+| 2.775 | 2775 | 0.6868        | -      |
+| 2.8   | 2800 | 0.6992        | -      |
+| 2.825 | 2825 | 0.6338        | -      |
+| 2.85  | 2850 | 0.6175        | -      |
+| 2.875 | 2875 | 0.6125        | -      |
+| 2.9   | 2900 | 0.6675        | -      |
+| 2.925 | 2925 | 0.6369        | -      |
+| 2.95  | 2950 | 0.6468        | -      |
+| 2.975 | 2975 | 0.6627        | -      |
+| 3.0   | 3000 | 0.6685        | 0.6765 |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.1
+- PyTorch: 2.3.0
+- Accelerate: 0.33.0
+- Datasets: 2.19.1
+- 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",
+}
+```
+
+#### 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.*
+-->

checkpoint-3000/config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "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": "4.44.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-3000/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.1",
+    "pytorch": "2.3.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

checkpoint-3000/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1ff47b03af3a3a2fa508b0c49eb4cf3ec4bfdd9963efd0cb054ede511e81029
+size 90864192

checkpoint-3000/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"
+  }
+]

checkpoint-3000/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ec71cd11be0835691f1106dde1f7104cd64ed999f51f6ef5234cd6d1211121a
+size 180604922

checkpoint-3000/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e70658d2dce830ec2775cd030a9a347894f70cb32d918a10e8b522855a7a92a4
+size 13990

checkpoint-3000/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2655ce6913f75602cb91c3bc837ffd6b125a095fbaa4102a582ad9459d0d7d9c
+size 1064

checkpoint-3000/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

checkpoint-3000/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
+  }
+}

checkpoint-3000/tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "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,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "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]"
+}

checkpoint-3000/trainer_state.json

@@ -0,0 +1,906 @@
+{
+  "best_metric": 0.6764523983001709,
+  "best_model_checkpoint": "autotrain-l21an-6mkt7/checkpoint-3000",
+  "epoch": 3.0,
+  "eval_steps": 500,
+  "global_step": 3000,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.025,
+      "grad_norm": 3.444312810897827,
+      "learning_rate": 2.4999999999999998e-06,
+      "loss": 0.6305,
+      "step": 25
+    },
+    {
+      "epoch": 0.05,
+      "grad_norm": 4.167919635772705,
+      "learning_rate": 4.9999999999999996e-06,
+      "loss": 0.7491,
+      "step": 50
+    },
+    {
+      "epoch": 0.075,
+      "grad_norm": 2.0621402263641357,
+      "learning_rate": 7.5e-06,
+      "loss": 0.6931,
+      "step": 75
+    },
+    {
+      "epoch": 0.1,
+      "grad_norm": 2.2826991081237793,
+      "learning_rate": 9.999999999999999e-06,
+      "loss": 0.7093,
+      "step": 100
+    },
+    {
+      "epoch": 0.125,
+      "grad_norm": 2.8610947132110596,
+      "learning_rate": 1.25e-05,
+      "loss": 0.631,
+      "step": 125
+    },
+    {
+      "epoch": 0.15,
+      "grad_norm": 2.2747015953063965,
+      "learning_rate": 1.5e-05,
+      "loss": 0.6127,
+      "step": 150
+    },
+    {
+      "epoch": 0.175,
+      "grad_norm": 1.3792694807052612,
+      "learning_rate": 1.7500000000000002e-05,
+      "loss": 0.5963,
+      "step": 175
+    },
+    {
+      "epoch": 0.2,
+      "grad_norm": 2.0398032665252686,
+      "learning_rate": 1.9999999999999998e-05,
+      "loss": 0.6556,
+      "step": 200
+    },
+    {
+      "epoch": 0.225,
+      "grad_norm": 1.9805101156234741,
+      "learning_rate": 2.25e-05,
+      "loss": 0.6831,
+      "step": 225
+    },
+    {
+      "epoch": 0.25,
+      "grad_norm": 1.3323885202407837,
+      "learning_rate": 2.5e-05,
+      "loss": 0.6389,
+      "step": 250
+    },
+    {
+      "epoch": 0.275,
+      "grad_norm": 1.6555352210998535,
+      "learning_rate": 2.75e-05,
+      "loss": 0.6913,
+      "step": 275
+    },
+    {
+      "epoch": 0.3,
+      "grad_norm": 1.4770078659057617,
+      "learning_rate": 3e-05,
+      "loss": 0.6447,
+      "step": 300
+    },
+    {
+      "epoch": 0.325,
+      "grad_norm": 1.9949244260787964,
+      "learning_rate": 2.9722222222222223e-05,
+      "loss": 0.6993,
+      "step": 325
+    },
+    {
+      "epoch": 0.35,
+      "grad_norm": 1.2942368984222412,
+      "learning_rate": 2.9444444444444445e-05,
+      "loss": 0.6581,
+      "step": 350
+    },
+    {
+      "epoch": 0.375,
+      "grad_norm": 1.8725063800811768,
+      "learning_rate": 2.9166666666666666e-05,
+      "loss": 0.6695,
+      "step": 375
+    },
+    {
+      "epoch": 0.4,
+      "grad_norm": 1.4848814010620117,
+      "learning_rate": 2.8888888888888888e-05,
+      "loss": 0.7076,
+      "step": 400
+    },
+    {
+      "epoch": 0.425,
+      "grad_norm": 1.406736969947815,
+      "learning_rate": 2.8611111111111113e-05,
+      "loss": 0.6301,
+      "step": 425
+    },
+    {
+      "epoch": 0.45,
+      "grad_norm": 1.2826756238937378,
+      "learning_rate": 2.8333333333333332e-05,
+      "loss": 0.6121,
+      "step": 450
+    },
+    {
+      "epoch": 0.475,
+      "grad_norm": 1.0705897808074951,
+      "learning_rate": 2.8055555555555557e-05,
+      "loss": 0.6439,
+      "step": 475
+    },
+    {
+      "epoch": 0.5,
+      "grad_norm": 1.7978061437606812,
+      "learning_rate": 2.777777777777778e-05,
+      "loss": 0.6782,
+      "step": 500
+    },
+    {
+      "epoch": 0.525,
+      "grad_norm": 1.2017405033111572,
+      "learning_rate": 2.75e-05,
+      "loss": 0.7025,
+      "step": 525
+    },
+    {
+      "epoch": 0.55,
+      "grad_norm": 1.4544589519500732,
+      "learning_rate": 2.7222222222222223e-05,
+      "loss": 0.7228,
+      "step": 550
+    },
+    {
+      "epoch": 0.575,
+      "grad_norm": 2.094083070755005,
+      "learning_rate": 2.6944444444444445e-05,
+      "loss": 0.6065,
+      "step": 575
+    },
+    {
+      "epoch": 0.6,
+      "grad_norm": 1.4134550094604492,
+      "learning_rate": 2.6666666666666667e-05,
+      "loss": 0.6496,
+      "step": 600
+    },
+    {
+      "epoch": 0.625,
+      "grad_norm": 1.1640647649765015,
+      "learning_rate": 2.6388888888888892e-05,
+      "loss": 0.6816,
+      "step": 625
+    },
+    {
+      "epoch": 0.65,
+      "grad_norm": 1.7982358932495117,
+      "learning_rate": 2.611111111111111e-05,
+      "loss": 0.6302,
+      "step": 650
+    },
+    {
+      "epoch": 0.675,
+      "grad_norm": 1.6336771249771118,
+      "learning_rate": 2.5833333333333336e-05,
+      "loss": 0.692,
+      "step": 675
+    },
+    {
+      "epoch": 0.7,
+      "grad_norm": 1.2203083038330078,
+      "learning_rate": 2.5555555555555557e-05,
+      "loss": 0.7533,
+      "step": 700
+    },
+    {
+      "epoch": 0.725,
+      "grad_norm": 1.4167596101760864,
+      "learning_rate": 2.5277777777777776e-05,
+      "loss": 0.6567,
+      "step": 725
+    },
+    {
+      "epoch": 0.75,
+      "grad_norm": 2.341327667236328,
+      "learning_rate": 2.5e-05,
+      "loss": 0.6472,
+      "step": 750
+    },
+    {
+      "epoch": 0.775,
+      "grad_norm": 1.1488889455795288,
+      "learning_rate": 2.4722222222222223e-05,
+      "loss": 0.6461,
+      "step": 775
+    },
+    {
+      "epoch": 0.8,
+      "grad_norm": 1.6008880138397217,
+      "learning_rate": 2.4444444444444445e-05,
+      "loss": 0.661,
+      "step": 800
+    },
+    {
+      "epoch": 0.825,
+      "grad_norm": 1.6397242546081543,
+      "learning_rate": 2.4166666666666667e-05,
+      "loss": 0.6897,
+      "step": 825
+    },
+    {
+      "epoch": 0.85,
+      "grad_norm": 1.567724347114563,
+      "learning_rate": 2.388888888888889e-05,
+      "loss": 0.6097,
+      "step": 850
+    },
+    {
+      "epoch": 0.875,
+      "grad_norm": 1.290542483329773,
+      "learning_rate": 2.3611111111111114e-05,
+      "loss": 0.6284,
+      "step": 875
+    },
+    {
+      "epoch": 0.9,
+      "grad_norm": 1.4457989931106567,
+      "learning_rate": 2.3333333333333336e-05,
+      "loss": 0.5923,
+      "step": 900
+    },
+    {
+      "epoch": 0.925,
+      "grad_norm": 1.1782793998718262,
+      "learning_rate": 2.3055555555555554e-05,
+      "loss": 0.6642,
+      "step": 925
+    },
+    {
+      "epoch": 0.95,
+      "grad_norm": 1.2478561401367188,
+      "learning_rate": 2.277777777777778e-05,
+      "loss": 0.6531,
+      "step": 950
+    },
+    {
+      "epoch": 0.975,
+      "grad_norm": 1.3522217273712158,
+      "learning_rate": 2.25e-05,
+      "loss": 0.6705,
+      "step": 975
+    },
+    {
+      "epoch": 1.0,
+      "grad_norm": 1.4155220985412598,
+      "learning_rate": 2.222222222222222e-05,
+      "loss": 0.7137,
+      "step": 1000
+    },
+    {
+      "epoch": 1.0,
+      "eval_loss": 0.6765261292457581,
+      "eval_runtime": 37.4092,
+      "eval_samples_per_second": 53.463,
+      "eval_steps_per_second": 3.341,
+      "step": 1000
+    },
+    {
+      "epoch": 1.025,
+      "grad_norm": 1.0540518760681152,
+      "learning_rate": 2.1944444444444445e-05,
+      "loss": 0.6601,
+      "step": 1025
+    },
+    {
+      "epoch": 1.05,
+      "grad_norm": 1.219468116760254,
+      "learning_rate": 2.1666666666666667e-05,
+      "loss": 0.6739,
+      "step": 1050
+    },
+    {
+      "epoch": 1.075,
+      "grad_norm": 1.1928082704544067,
+      "learning_rate": 2.138888888888889e-05,
+      "loss": 0.6487,
+      "step": 1075
+    },
+    {
+      "epoch": 1.1,
+      "grad_norm": 1.0191409587860107,
+      "learning_rate": 2.111111111111111e-05,
+      "loss": 0.6864,
+      "step": 1100
+    },
+    {
+      "epoch": 1.125,
+      "grad_norm": 1.0731534957885742,
+      "learning_rate": 2.0833333333333333e-05,
+      "loss": 0.7744,
+      "step": 1125
+    },
+    {
+      "epoch": 1.15,
+      "grad_norm": 1.1843361854553223,
+      "learning_rate": 2.0555555555555558e-05,
+      "loss": 0.6698,
+      "step": 1150
+    },
+    {
+      "epoch": 1.175,
+      "grad_norm": 1.1600492000579834,
+      "learning_rate": 2.027777777777778e-05,
+      "loss": 0.6421,
+      "step": 1175
+    },
+    {
+      "epoch": 1.2,
+      "grad_norm": 1.3744503259658813,
+      "learning_rate": 1.9999999999999998e-05,
+      "loss": 0.633,
+      "step": 1200
+    },
+    {
+      "epoch": 1.225,
+      "grad_norm": 0.8186588287353516,
+      "learning_rate": 1.9722222222222224e-05,
+      "loss": 0.678,
+      "step": 1225
+    },
+    {
+      "epoch": 1.25,
+      "grad_norm": 1.2602007389068604,
+      "learning_rate": 1.9444444444444445e-05,
+      "loss": 0.6264,
+      "step": 1250
+    },
+    {
+      "epoch": 1.275,
+      "grad_norm": 1.5430500507354736,
+      "learning_rate": 1.9166666666666667e-05,
+      "loss": 0.721,
+      "step": 1275
+    },
+    {
+      "epoch": 1.3,
+      "grad_norm": 1.6438603401184082,
+      "learning_rate": 1.888888888888889e-05,
+      "loss": 0.6736,
+      "step": 1300
+    },
+    {
+      "epoch": 1.325,
+      "grad_norm": 1.1491776704788208,
+      "learning_rate": 1.861111111111111e-05,
+      "loss": 0.5332,
+      "step": 1325
+    },
+    {
+      "epoch": 1.35,
+      "grad_norm": 1.087183952331543,
+      "learning_rate": 1.8333333333333336e-05,
+      "loss": 0.6576,
+      "step": 1350
+    },
+    {
+      "epoch": 1.375,
+      "grad_norm": 1.6351675987243652,
+      "learning_rate": 1.8055555555555555e-05,
+      "loss": 0.6625,
+      "step": 1375
+    },
+    {
+      "epoch": 1.4,
+      "grad_norm": 1.5467578172683716,
+      "learning_rate": 1.7777777777777777e-05,
+      "loss": 0.7248,
+      "step": 1400
+    },
+    {
+      "epoch": 1.425,
+      "grad_norm": 1.1913565397262573,
+      "learning_rate": 1.7500000000000002e-05,
+      "loss": 0.6188,
+      "step": 1425
+    },
+    {
+      "epoch": 1.45,
+      "grad_norm": 1.1346111297607422,
+      "learning_rate": 1.7222222222222224e-05,
+      "loss": 0.6452,
+      "step": 1450
+    },
+    {
+      "epoch": 1.475,
+      "grad_norm": 1.3555978536605835,
+      "learning_rate": 1.6944444444444442e-05,
+      "loss": 0.7024,
+      "step": 1475
+    },
+    {
+      "epoch": 1.5,
+      "grad_norm": 0.8716872930526733,
+      "learning_rate": 1.6666666666666667e-05,
+      "loss": 0.7005,
+      "step": 1500
+    },
+    {
+      "epoch": 1.525,
+      "grad_norm": 0.9957846999168396,
+      "learning_rate": 1.638888888888889e-05,
+      "loss": 0.6219,
+      "step": 1525
+    },
+    {
+      "epoch": 1.55,
+      "grad_norm": 2.1997838020324707,
+      "learning_rate": 1.6111111111111115e-05,
+      "loss": 0.6525,
+      "step": 1550
+    },
+    {
+      "epoch": 1.575,
+      "grad_norm": 1.3860341310501099,
+      "learning_rate": 1.5833333333333333e-05,
+      "loss": 0.6718,
+      "step": 1575
+    },
+    {
+      "epoch": 1.6,
+      "grad_norm": 0.8452956676483154,
+      "learning_rate": 1.5555555555555555e-05,
+      "loss": 0.6738,
+      "step": 1600
+    },
+    {
+      "epoch": 1.625,
+      "grad_norm": 0.9731984734535217,
+      "learning_rate": 1.527777777777778e-05,
+      "loss": 0.6558,
+      "step": 1625
+    },
+    {
+      "epoch": 1.65,
+      "grad_norm": 1.831750750541687,
+      "learning_rate": 1.5e-05,
+      "loss": 0.6236,
+      "step": 1650
+    },
+    {
+      "epoch": 1.675,
+      "grad_norm": 1.6755101680755615,
+      "learning_rate": 1.4722222222222222e-05,
+      "loss": 0.7126,
+      "step": 1675
+    },
+    {
+      "epoch": 1.7,
+      "grad_norm": 1.3757505416870117,
+      "learning_rate": 1.4444444444444444e-05,
+      "loss": 0.6822,
+      "step": 1700
+    },
+    {
+      "epoch": 1.725,
+      "grad_norm": 1.377435326576233,
+      "learning_rate": 1.4166666666666666e-05,
+      "loss": 0.6324,
+      "step": 1725
+    },
+    {
+      "epoch": 1.75,
+      "grad_norm": 1.0001251697540283,
+      "learning_rate": 1.388888888888889e-05,
+      "loss": 0.7036,
+      "step": 1750
+    },
+    {
+      "epoch": 1.775,
+      "grad_norm": 1.0013527870178223,
+      "learning_rate": 1.3611111111111111e-05,
+      "loss": 0.6765,
+      "step": 1775
+    },
+    {
+      "epoch": 1.8,
+      "grad_norm": 1.0746055841445923,
+      "learning_rate": 1.3333333333333333e-05,
+      "loss": 0.654,
+      "step": 1800
+    },
+    {
+      "epoch": 1.825,
+      "grad_norm": 0.6743106842041016,
+      "learning_rate": 1.3055555555555555e-05,
+      "loss": 0.6923,
+      "step": 1825
+    },
+    {
+      "epoch": 1.85,
+      "grad_norm": 0.9659077525138855,
+      "learning_rate": 1.2777777777777779e-05,
+      "loss": 0.6976,
+      "step": 1850
+    },
+    {
+      "epoch": 1.875,
+      "grad_norm": 0.7309139966964722,
+      "learning_rate": 1.25e-05,
+      "loss": 0.6904,
+      "step": 1875
+    },
+    {
+      "epoch": 1.9,
+      "grad_norm": 0.6239315271377563,
+      "learning_rate": 1.2222222222222222e-05,
+      "loss": 0.6307,
+      "step": 1900
+    },
+    {
+      "epoch": 1.925,
+      "grad_norm": 1.3958375453948975,
+      "learning_rate": 1.1944444444444444e-05,
+      "loss": 0.6437,
+      "step": 1925
+    },
+    {
+      "epoch": 1.95,
+      "grad_norm": 0.7617831230163574,
+      "learning_rate": 1.1666666666666668e-05,
+      "loss": 0.6333,
+      "step": 1950
+    },
+    {
+      "epoch": 1.975,
+      "grad_norm": 0.8432300686836243,
+      "learning_rate": 1.138888888888889e-05,
+      "loss": 0.6214,
+      "step": 1975
+    },
+    {
+      "epoch": 2.0,
+      "grad_norm": 1.236924409866333,
+      "learning_rate": 1.111111111111111e-05,
+      "loss": 0.6424,
+      "step": 2000
+    },
+    {
+      "epoch": 2.0,
+      "eval_loss": 0.6764588952064514,
+      "eval_runtime": 38.544,
+      "eval_samples_per_second": 51.889,
+      "eval_steps_per_second": 3.243,
+      "step": 2000
+    },
+    {
+      "epoch": 2.025,
+      "grad_norm": 1.2284152507781982,
+      "learning_rate": 1.0833333333333334e-05,
+      "loss": 0.7041,
+      "step": 2025
+    },
+    {
+      "epoch": 2.05,
+      "grad_norm": 0.9430116415023804,
+      "learning_rate": 1.0555555555555555e-05,
+      "loss": 0.7112,
+      "step": 2050
+    },
+    {
+      "epoch": 2.075,
+      "grad_norm": 0.5471211075782776,
+      "learning_rate": 1.0277777777777779e-05,
+      "loss": 0.6696,
+      "step": 2075
+    },
+    {
+      "epoch": 2.1,
+      "grad_norm": 0.9567949771881104,
+      "learning_rate": 9.999999999999999e-06,
+      "loss": 0.6739,
+      "step": 2100
+    },
+    {
+      "epoch": 2.125,
+      "grad_norm": 0.633762001991272,
+      "learning_rate": 9.722222222222223e-06,
+      "loss": 0.6315,
+      "step": 2125
+    },
+    {
+      "epoch": 2.15,
+      "grad_norm": 1.0539363622665405,
+      "learning_rate": 9.444444444444445e-06,
+      "loss": 0.7649,
+      "step": 2150
+    },
+    {
+      "epoch": 2.175,
+      "grad_norm": 0.9735732078552246,
+      "learning_rate": 9.166666666666668e-06,
+      "loss": 0.7079,
+      "step": 2175
+    },
+    {
+      "epoch": 2.2,
+      "grad_norm": 1.3620977401733398,
+      "learning_rate": 8.888888888888888e-06,
+      "loss": 0.6549,
+      "step": 2200
+    },
+    {
+      "epoch": 2.225,
+      "grad_norm": 0.9268941879272461,
+      "learning_rate": 8.611111111111112e-06,
+      "loss": 0.6548,
+      "step": 2225
+    },
+    {
+      "epoch": 2.25,
+      "grad_norm": 0.4519413709640503,
+      "learning_rate": 8.333333333333334e-06,
+      "loss": 0.6647,
+      "step": 2250
+    },
+    {
+      "epoch": 2.275,
+      "grad_norm": 0.8613296747207642,
+      "learning_rate": 8.055555555555557e-06,
+      "loss": 0.7568,
+      "step": 2275
+    },
+    {
+      "epoch": 2.3,
+      "grad_norm": 1.1156052350997925,
+      "learning_rate": 7.777777777777777e-06,
+      "loss": 0.6659,
+      "step": 2300
+    },
+    {
+      "epoch": 2.325,
+      "grad_norm": 0.8070225119590759,
+      "learning_rate": 7.5e-06,
+      "loss": 0.604,
+      "step": 2325
+    },
+    {
+      "epoch": 2.35,
+      "grad_norm": 1.2144221067428589,
+      "learning_rate": 7.222222222222222e-06,
+      "loss": 0.6342,
+      "step": 2350
+    },
+    {
+      "epoch": 2.375,
+      "grad_norm": 0.8116927742958069,
+      "learning_rate": 6.944444444444445e-06,
+      "loss": 0.6891,
+      "step": 2375
+    },
+    {
+      "epoch": 2.4,
+      "grad_norm": 0.8394978642463684,
+      "learning_rate": 6.666666666666667e-06,
+      "loss": 0.6856,
+      "step": 2400
+    },
+    {
+      "epoch": 2.425,
+      "grad_norm": 1.1582024097442627,
+      "learning_rate": 6.388888888888889e-06,
+      "loss": 0.6683,
+      "step": 2425
+    },
+    {
+      "epoch": 2.45,
+      "grad_norm": 0.9621151089668274,
+      "learning_rate": 6.111111111111111e-06,
+      "loss": 0.678,
+      "step": 2450
+    },
+    {
+      "epoch": 2.475,
+      "grad_norm": 1.0509181022644043,
+      "learning_rate": 5.833333333333334e-06,
+      "loss": 0.7102,
+      "step": 2475
+    },
+    {
+      "epoch": 2.5,
+      "grad_norm": 0.7675669193267822,
+      "learning_rate": 5.555555555555555e-06,
+      "loss": 0.6606,
+      "step": 2500
+    },
+    {
+      "epoch": 2.525,
+      "grad_norm": 0.9356604218482971,
+      "learning_rate": 5.277777777777778e-06,
+      "loss": 0.6634,
+      "step": 2525
+    },
+    {
+      "epoch": 2.55,
+      "grad_norm": 1.1379098892211914,
+      "learning_rate": 4.9999999999999996e-06,
+      "loss": 0.6443,
+      "step": 2550
+    },
+    {
+      "epoch": 2.575,
+      "grad_norm": 1.0013926029205322,
+      "learning_rate": 4.722222222222222e-06,
+      "loss": 0.6122,
+      "step": 2575
+    },
+    {
+      "epoch": 2.6,
+      "grad_norm": 0.771693229675293,
+      "learning_rate": 4.444444444444444e-06,
+      "loss": 0.6926,
+      "step": 2600
+    },
+    {
+      "epoch": 2.625,
+      "grad_norm": 0.7376611232757568,
+      "learning_rate": 4.166666666666667e-06,
+      "loss": 0.5957,
+      "step": 2625
+    },
+    {
+      "epoch": 2.65,
+      "grad_norm": 0.7340726256370544,
+      "learning_rate": 3.888888888888889e-06,
+      "loss": 0.6933,
+      "step": 2650
+    },
+    {
+      "epoch": 2.675,
+      "grad_norm": 0.7760947942733765,
+      "learning_rate": 3.611111111111111e-06,
+      "loss": 0.691,
+      "step": 2675
+    },
+    {
+      "epoch": 2.7,
+      "grad_norm": 0.9809922575950623,
+      "learning_rate": 3.3333333333333333e-06,
+      "loss": 0.7015,
+      "step": 2700
+    },
+    {
+      "epoch": 2.725,
+      "grad_norm": 0.9861670732498169,
+      "learning_rate": 3.0555555555555556e-06,
+      "loss": 0.7057,
+      "step": 2725
+    },
+    {
+      "epoch": 2.75,
+      "grad_norm": 0.8055828809738159,
+      "learning_rate": 2.7777777777777775e-06,
+      "loss": 0.6386,
+      "step": 2750
+    },
+    {
+      "epoch": 2.775,
+      "grad_norm": 1.0951838493347168,
+      "learning_rate": 2.4999999999999998e-06,
+      "loss": 0.6868,
+      "step": 2775
+    },
+    {
+      "epoch": 2.8,
+      "grad_norm": 1.086242437362671,
+      "learning_rate": 2.222222222222222e-06,
+      "loss": 0.6992,
+      "step": 2800
+    },
+    {
+      "epoch": 2.825,
+      "grad_norm": 0.6613348126411438,
+      "learning_rate": 1.9444444444444444e-06,
+      "loss": 0.6338,
+      "step": 2825
+    },
+    {
+      "epoch": 2.85,
+      "grad_norm": 0.944501519203186,
+      "learning_rate": 1.6666666666666667e-06,
+      "loss": 0.6175,
+      "step": 2850
+    },
+    {
+      "epoch": 2.875,
+      "grad_norm": 0.5407629609107971,
+      "learning_rate": 1.3888888888888887e-06,
+      "loss": 0.6125,
+      "step": 2875
+    },
+    {
+      "epoch": 2.9,
+      "grad_norm": 1.0618243217468262,
+      "learning_rate": 1.111111111111111e-06,
+      "loss": 0.6675,
+      "step": 2900
+    },
+    {
+      "epoch": 2.925,
+      "grad_norm": 0.6185476183891296,
+      "learning_rate": 8.333333333333333e-07,
+      "loss": 0.6369,
+      "step": 2925
+    },
+    {
+      "epoch": 2.95,
+      "grad_norm": 0.9023645520210266,
+      "learning_rate": 5.555555555555555e-07,
+      "loss": 0.6468,
+      "step": 2950
+    },
+    {
+      "epoch": 2.975,
+      "grad_norm": 1.4191973209381104,
+      "learning_rate": 2.7777777777777776e-07,
+      "loss": 0.6627,
+      "step": 2975
+    },
+    {
+      "epoch": 3.0,
+      "grad_norm": 0.7791981101036072,
+      "learning_rate": 0.0,
+      "loss": 0.6685,
+      "step": 3000
+    },
+    {
+      "epoch": 3.0,
+      "eval_loss": 0.6764523983001709,
+      "eval_runtime": 38.9354,
+      "eval_samples_per_second": 51.367,
+      "eval_steps_per_second": 3.21,
+      "step": 3000
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 3000,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 3,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "EarlyStoppingCallback": {
+      "args": {
+        "early_stopping_patience": 5,
+        "early_stopping_threshold": 0.01
+      },
+      "attributes": {
+        "early_stopping_patience_counter": 0
+      }
+    },
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 8,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-3000/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:589663a5d5d4c2ad6cadb41f9c6f0fc1833a3eb1b7861c2bbe91f737ce19deb2
+size 5432

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "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": "4.44.1",
+  "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": "4.44.1",
+    "pytorch": "2.3.0"
+  },
+  "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:a1ff47b03af3a3a2fa508b0c49eb4cf3ec4bfdd9963efd0cb054ede511e81029
+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"
+  }
+]

runs/Aug24_14-17-20_r-booster-team-gpt2-vq6zx4bx-737ae-o6rpl/events.out.tfevents.1724509043.r-booster-team-gpt2-vq6zx4bx-737ae-o6rpl.119.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8747e8ca8b23e57bc4cf33ce87190fdbbd43a40de6ccf2d3003d082ffb8768b7
-size 24657
+oid sha256:1958b35b9a85b4ad73968ca3f6903dd3a7abac6fe2551122b59276de851c8307
+size 30768

runs/Aug24_14-17-20_r-booster-team-gpt2-vq6zx4bx-737ae-o6rpl/events.out.tfevents.1724511395.r-booster-team-gpt2-vq6zx4bx-737ae-o6rpl.119.1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:959a880d6430fb04462fc025bd34cdd0f0d9e6298bec70f0bf102dae21c923e4
+size 359

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 +1,64 @@
+{
+  "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,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "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]"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:589663a5d5d4c2ad6cadb41f9c6f0fc1833a3eb1b7861c2bbe91f737ce19deb2
+size 5432

training_params.json

@@ -0,0 +1,33 @@
+{
+    "data_path": "autotrain-l21an-6mkt7/autotrain-data",
+    "model": "sentence-transformers/all-MiniLM-L6-v2",
+    "lr": 3e-05,
+    "epochs": 3,
+    "max_seq_length": 128,
+    "batch_size": 8,
+    "warmup_ratio": 0.1,
+    "gradient_accumulation": 1,
+    "optimizer": "adamw_torch",
+    "scheduler": "linear",
+    "weight_decay": 0.0,
+    "max_grad_norm": 1.0,
+    "seed": 42,
+    "train_split": "train",
+    "valid_split": "validation",
+    "logging_steps": -1,
+    "project_name": "autotrain-l21an-6mkt7",
+    "auto_find_batch_size": false,
+    "mixed_precision": "fp16",
+    "save_total_limit": 1,
+    "push_to_hub": true,
+    "eval_strategy": "epoch",
+    "username": "booster-team",
+    "log": "tensorboard",
+    "early_stopping_patience": 5,
+    "early_stopping_threshold": 0.01,
+    "trainer": "qa",
+    "sentence1_column": "autotrain_sentence1",
+    "sentence2_column": "autotrain_sentence2",
+    "sentence3_column": "autotrain_sentence3",
+    "target_column": "autotrain_target"
+}