Add new SentenceTransformer model.

1_Pooling/config.json

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

README.md

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+---
+base_model: BAAI/bge-base-en-v1.5
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:48
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Users are entitled to a refund for excess payments after necessary
+    deductions, provided that payments were not processed to a wrong account due to
+    user error.
+  sentences:
+  - What is the timeline for the delivery of the documentary film as outlined in this
+    contract?
+  - Under what circumstances can a user receive a refund for multiple payments made
+    for a single order?
+  - What are the Payment Terms for the Batteries?
+- source_sentence: Users can contact Customer Care before confirmation to request
+    a refund for offline services or reschedule for online services, subject to the
+    platform's discretion.
+  sentences:
+  - How does Paratalks handle refund requests made before a service professional confirms
+    a booking?
+  - How should proprietary and confidential information disclosed under the Agreement
+    be treated by the Parties?
+  - When does this Agreement terminate?
+- source_sentence: If there is any unreasonable delay in the refund process, the User
+    can report it to Customer Care at contact@paratalks.in or +91-9116768791.
+  sentences:
+  - What should a User do if there is an unreasonable delay in the refund process?
+  - What are the confidentiality provisions in this contract?
+  - What are the specified payment terms for the photography services under this contract?
+- source_sentence: The refund (if permitted by the Platform) shall be processed after
+    deductions, which may include transaction charges levied by the bank and/or the
+    payment gateway, as well as any other charges incurred by the Platform for facilitating
+    the payment or refund.
+  sentences:
+  - What are the conditions under which a user is not entitled to a refund according
+    to Paratalks' refund policy?
+  - What is the jurisdiction and governing law applicable to this contract?
+  - How are refunds processed if permitted by the Platform?
+- source_sentence: This Agreement shall be governed by and construed in accordance
+    with the laws of Indiana. Any dispute arising out of or in connection with this
+    Agreement shall be resolved through good faith negotiations between the Parties
+    and will be subject to the jurisdiction of the courts of Dania.
+  sentences:
+  - Under what condition will the User not be entitled to a refund if the payment
+    is processed to a wrong Account?
+  - What events constitute Force Majeure under this Agreement?
+  - Under which laws is the Battery Supply Agreement governed and how are disputes
+    resolved?
+model-index:
+- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8333333333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8333333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8333333333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8333333333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27777777777777773
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16666666666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8333333333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8333333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8333333333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.892701197851337
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8611111111111112
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8611111111111112
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8333333333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8333333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8333333333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8333333333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27777777777777773
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16666666666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8333333333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8333333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8333333333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.892701197851337
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8611111111111112
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8611111111111112
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8333333333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8333333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8333333333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8333333333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27777777777777773
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16666666666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8333333333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8333333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8333333333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.892701197851337
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8611111111111112
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8611111111111112
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8333333333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8333333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8333333333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8333333333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27777777777777773
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16666666666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8333333333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8333333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8333333333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8859108127976215
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8541666666666666
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8541666666666666
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8333333333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8333333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8333333333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8333333333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27777777777777773
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16666666666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8333333333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8333333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8333333333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8835049992773302
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8518518518518517
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8518518518518517
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-base-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (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("vineet10/fm1")
+# Run inference
+sentences = [
+    'This Agreement shall be governed by and construed in accordance with the laws of Indiana. Any dispute arising out of or in connection with this Agreement shall be resolved through good faith negotiations between the Parties and will be subject to the jurisdiction of the courts of Dania.',
+    'Under which laws is the Battery Supply Agreement governed and how are disputes resolved?',
+    'What events constitute Force Majeure under this Agreement?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `dim_768`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8333     |
+| cosine_accuracy@3   | 0.8333     |
+| cosine_accuracy@5   | 0.8333     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8333     |
+| cosine_precision@3  | 0.2778     |
+| cosine_precision@5  | 0.1667     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8333     |
+| cosine_recall@3     | 0.8333     |
+| cosine_recall@5     | 0.8333     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.8927     |
+| cosine_mrr@10       | 0.8611     |
+| **cosine_map@100**  | **0.8611** |
+
+#### Information Retrieval
+* Dataset: `dim_512`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8333     |
+| cosine_accuracy@3   | 0.8333     |
+| cosine_accuracy@5   | 0.8333     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8333     |
+| cosine_precision@3  | 0.2778     |
+| cosine_precision@5  | 0.1667     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8333     |
+| cosine_recall@3     | 0.8333     |
+| cosine_recall@5     | 0.8333     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.8927     |
+| cosine_mrr@10       | 0.8611     |
+| **cosine_map@100**  | **0.8611** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8333     |
+| cosine_accuracy@3   | 0.8333     |
+| cosine_accuracy@5   | 0.8333     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8333     |
+| cosine_precision@3  | 0.2778     |
+| cosine_precision@5  | 0.1667     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8333     |
+| cosine_recall@3     | 0.8333     |
+| cosine_recall@5     | 0.8333     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.8927     |
+| cosine_mrr@10       | 0.8611     |
+| **cosine_map@100**  | **0.8611** |
+
+#### Information Retrieval
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8333     |
+| cosine_accuracy@3   | 0.8333     |
+| cosine_accuracy@5   | 0.8333     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8333     |
+| cosine_precision@3  | 0.2778     |
+| cosine_precision@5  | 0.1667     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8333     |
+| cosine_recall@3     | 0.8333     |
+| cosine_recall@5     | 0.8333     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.8859     |
+| cosine_mrr@10       | 0.8542     |
+| **cosine_map@100**  | **0.8542** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8333     |
+| cosine_accuracy@3   | 0.8333     |
+| cosine_accuracy@5   | 0.8333     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8333     |
+| cosine_precision@3  | 0.2778     |
+| cosine_precision@5  | 0.1667     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8333     |
+| cosine_recall@3     | 0.8333     |
+| cosine_recall@5     | 0.8333     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.8835     |
+| cosine_mrr@10       | 0.8519     |
+| **cosine_map@100**  | **0.8519** |
+
+<!--
+## 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: 48 training samples
+* Columns: <code>context</code> and <code>question</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | context                                                                            | question                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                           |
+  | details | <ul><li>min: 18 tokens</li><li>mean: 39.58 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.9 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | context                                                                                                                                                                                                                                                                    | question                                                                                                    |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|
+  | <code>The Client will pay a flat fee of Rs. 52,000/-, with 50% (Rs. 26,000/-) due upon signing the agreement and the remaining 50% due one week after completion of pre-production. Payment delays will result in proportional delays in data delivery and editing.</code> | <code>What are the specified payment terms for the photography services under this contract?</code>         |
+  | <code>Users can report delays to Customer Care and expect an automatic refund within 3-4 business days if services are canceled or rescheduled by the platform.</code>                                                                                                     | <code>What actions can a user take if the platform is unable to fulfill a successfully placed order?</code> |
+  | <code>Signed by James Hira, Managing Director of Electric Vehicle Battery Supplier Pvt. Ltd, and Managing Director of Best Car Manufacturer Pvt. Ltd</code>                                                                                                                | <code>Who signed the Battery Supply Agreement on behalf of the Supplier and the Manufacturer?</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`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-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`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
+|:-----:|:----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0     | 0    | 0.8542                 | 0.8611                 | 0.8611                 | 0.8519                | 0.8611                 |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### 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 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7d52035a750e4a6e4637ada8fd9a191afa0b993759df025310f600c1d1855e7a
+size 437951328

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 +1,57 @@
+{
+  "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]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}