Add new SentenceTransformer model.
Browse files- 1_Pooling/config.json +10 -0
- README.md +521 -0
- config.json +26 -0
- config_sentence_transformers.json +10 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +44 -0
- tokenizer.json +0 -0
- tokenizer_config.json +66 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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1 |
+
---
|
2 |
+
base_model: dbourget/philai-embeddings-2.0
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library_name: sentence-transformers
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+
metrics:
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5 |
+
- cosine_accuracy
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+
- dot_accuracy
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+
- manhattan_accuracy
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- euclidean_accuracy
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- max_accuracy
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:9504
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- loss:TripletLoss
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widget:
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- source_sentence: cap product
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sentences:
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- method of adjoining a chain of degree p with a co-chain of degree q, where q is
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less than or equal to p, to form a composite chain of degree p-q
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+
- 'Ontology '
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- hat commodity
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- source_sentence: cognitivism
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sentences:
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- supporting cognitive science
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- study of changes in organisms caused by modification of gene expression rather
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than alteration of the genetic code
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- 'the idea that mind works like an algorithmic symbol manipulation '
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+
- source_sentence: doxastic voluntarism
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sentences:
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- Land surrounded by water
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- belief one is free
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- the ability to will beliefs
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- source_sentence: conceptual role
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sentences:
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- concept
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- inferential role
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- 'Theory of knowledge '
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- source_sentence: scientific revolutions
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sentences:
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- scientific realism
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- Universal moral principles govern legal systems
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- paradigm shifts
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model-index:
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- name: SentenceTransformer based on dbourget/philai-embeddings-2.0
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+
results:
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- task:
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type: triplet
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name: Triplet
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dataset:
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name: beatai dev
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type: beatai-dev
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metrics:
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- type: cosine_accuracy
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value: 0.8215488215488216
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name: Cosine Accuracy
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- type: dot_accuracy
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value: 0.24494949494949494
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name: Dot Accuracy
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- type: manhattan_accuracy
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value: 0.835016835016835
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name: Manhattan Accuracy
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- type: euclidean_accuracy
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value: 0.8341750841750841
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name: Euclidean Accuracy
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- type: max_accuracy
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value: 0.835016835016835
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name: Max Accuracy
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---
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+
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# SentenceTransformer based on dbourget/philai-embeddings-2.0
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/philai-embeddings-2.0](https://huggingface.co/dbourget/philai-embeddings-2.0). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
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+
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## Model Details
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+
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [dbourget/philai-embeddings-2.0](https://huggingface.co/dbourget/philai-embeddings-2.0) <!-- at revision d9add3b37c9bea5883418ac3f1d45cb29fe3a1dc -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 1024 tokens
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 1024, '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})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-30e")
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# Run inference
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sentences = [
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'scientific revolutions',
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'paradigm shifts',
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'scientific realism',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 1024]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
|
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+
### Direct Usage (Transformers)
|
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+
|
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+
<details><summary>Click to see the direct usage in Transformers</summary>
|
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+
|
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+
</details>
|
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+
-->
|
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+
|
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<!--
|
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### Downstream Usage (Sentence Transformers)
|
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|
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You can finetune this model on your own dataset.
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+
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<details><summary>Click to expand</summary>
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|
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</details>
|
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-->
|
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+
|
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<!--
|
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### Out-of-Scope Use
|
156 |
+
|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
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-->
|
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+
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## Evaluation
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+
|
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### Metrics
|
163 |
+
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#### Triplet
|
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* Dataset: `beatai-dev`
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
|
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| Metric | Value |
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|:-------------------|:----------|
|
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| cosine_accuracy | 0.8215 |
|
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| dot_accuracy | 0.2449 |
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| manhattan_accuracy | 0.835 |
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| euclidean_accuracy | 0.8342 |
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| **max_accuracy** | **0.835** |
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+
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<!--
|
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## Bias, Risks and Limitations
|
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+
|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
|
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+
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<!--
|
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### Recommendations
|
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+
|
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
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-->
|
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+
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## Training Details
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+
|
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 138
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- `per_device_eval_batch_size`: 138
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- `learning_rate`: 1e-06
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- `weight_decay`: 0.01
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- `num_train_epochs`: 20
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- `lr_scheduler_type`: constant
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- `bf16`: True
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- `dataloader_drop_last`: True
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- `resume_from_checkpoint`: True
|
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+
|
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#### All Hyperparameters
|
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<details><summary>Click to expand</summary>
|
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|
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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+
- `per_device_train_batch_size`: 138
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- `per_device_eval_batch_size`: 138
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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+
- `gradient_accumulation_steps`: 1
|
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- `eval_accumulation_steps`: None
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+
- `torch_empty_cache_steps`: None
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+
- `learning_rate`: 1e-06
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- `weight_decay`: 0.01
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
|
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- `adam_epsilon`: 1e-08
|
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- `max_grad_norm`: 1.0
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+
- `num_train_epochs`: 20
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- `max_steps`: -1
|
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+
- `lr_scheduler_type`: constant
|
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- `lr_scheduler_kwargs`: {}
|
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- `warmup_ratio`: 0
|
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- `warmup_steps`: 0
|
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- `log_level`: passive
|
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- `log_level_replica`: warning
|
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- `log_on_each_node`: True
|
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- `logging_nan_inf_filter`: True
|
234 |
+
- `save_safetensors`: True
|
235 |
+
- `save_on_each_node`: False
|
236 |
+
- `save_only_model`: False
|
237 |
+
- `restore_callback_states_from_checkpoint`: False
|
238 |
+
- `no_cuda`: False
|
239 |
+
- `use_cpu`: False
|
240 |
+
- `use_mps_device`: False
|
241 |
+
- `seed`: 42
|
242 |
+
- `data_seed`: None
|
243 |
+
- `jit_mode_eval`: False
|
244 |
+
- `use_ipex`: False
|
245 |
+
- `bf16`: True
|
246 |
+
- `fp16`: False
|
247 |
+
- `fp16_opt_level`: O1
|
248 |
+
- `half_precision_backend`: auto
|
249 |
+
- `bf16_full_eval`: False
|
250 |
+
- `fp16_full_eval`: False
|
251 |
+
- `tf32`: None
|
252 |
+
- `local_rank`: 0
|
253 |
+
- `ddp_backend`: None
|
254 |
+
- `tpu_num_cores`: None
|
255 |
+
- `tpu_metrics_debug`: False
|
256 |
+
- `debug`: []
|
257 |
+
- `dataloader_drop_last`: True
|
258 |
+
- `dataloader_num_workers`: 0
|
259 |
+
- `dataloader_prefetch_factor`: 2
|
260 |
+
- `past_index`: -1
|
261 |
+
- `disable_tqdm`: False
|
262 |
+
- `remove_unused_columns`: True
|
263 |
+
- `label_names`: None
|
264 |
+
- `load_best_model_at_end`: False
|
265 |
+
- `ignore_data_skip`: False
|
266 |
+
- `fsdp`: []
|
267 |
+
- `fsdp_min_num_params`: 0
|
268 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
269 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
270 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
271 |
+
- `deepspeed`: None
|
272 |
+
- `label_smoothing_factor`: 0.0
|
273 |
+
- `optim`: adamw_torch
|
274 |
+
- `optim_args`: None
|
275 |
+
- `adafactor`: False
|
276 |
+
- `group_by_length`: False
|
277 |
+
- `length_column_name`: length
|
278 |
+
- `ddp_find_unused_parameters`: None
|
279 |
+
- `ddp_bucket_cap_mb`: None
|
280 |
+
- `ddp_broadcast_buffers`: False
|
281 |
+
- `dataloader_pin_memory`: True
|
282 |
+
- `dataloader_persistent_workers`: False
|
283 |
+
- `skip_memory_metrics`: True
|
284 |
+
- `use_legacy_prediction_loop`: False
|
285 |
+
- `push_to_hub`: False
|
286 |
+
- `resume_from_checkpoint`: True
|
287 |
+
- `hub_model_id`: None
|
288 |
+
- `hub_strategy`: every_save
|
289 |
+
- `hub_private_repo`: False
|
290 |
+
- `hub_always_push`: False
|
291 |
+
- `gradient_checkpointing`: False
|
292 |
+
- `gradient_checkpointing_kwargs`: None
|
293 |
+
- `include_inputs_for_metrics`: False
|
294 |
+
- `eval_do_concat_batches`: True
|
295 |
+
- `fp16_backend`: auto
|
296 |
+
- `push_to_hub_model_id`: None
|
297 |
+
- `push_to_hub_organization`: None
|
298 |
+
- `mp_parameters`:
|
299 |
+
- `auto_find_batch_size`: False
|
300 |
+
- `full_determinism`: False
|
301 |
+
- `torchdynamo`: None
|
302 |
+
- `ray_scope`: last
|
303 |
+
- `ddp_timeout`: 1800
|
304 |
+
- `torch_compile`: False
|
305 |
+
- `torch_compile_backend`: None
|
306 |
+
- `torch_compile_mode`: None
|
307 |
+
- `dispatch_batches`: None
|
308 |
+
- `split_batches`: None
|
309 |
+
- `include_tokens_per_second`: False
|
310 |
+
- `include_num_input_tokens_seen`: False
|
311 |
+
- `neftune_noise_alpha`: None
|
312 |
+
- `optim_target_modules`: None
|
313 |
+
- `batch_eval_metrics`: False
|
314 |
+
- `eval_on_start`: False
|
315 |
+
- `use_liger_kernel`: False
|
316 |
+
- `eval_use_gather_object`: False
|
317 |
+
- `batch_sampler`: batch_sampler
|
318 |
+
- `multi_dataset_batch_sampler`: proportional
|
319 |
+
|
320 |
+
</details>
|
321 |
+
|
322 |
+
### Training Logs
|
323 |
+
<details><summary>Click to expand</summary>
|
324 |
+
|
325 |
+
| Epoch | Step | Training Loss | loss | beatai-dev_max_accuracy |
|
326 |
+
|:-------:|:----:|:-------------:|:------:|:-----------------------:|
|
327 |
+
| 0 | 0 | - | - | 0.8308 |
|
328 |
+
| 0.1471 | 10 | 1.056 | - | - |
|
329 |
+
| 0.2941 | 20 | 1.0992 | - | - |
|
330 |
+
| 0.4412 | 30 | 1.1678 | - | - |
|
331 |
+
| 0.5882 | 40 | 1.1586 | - | - |
|
332 |
+
| 0.7353 | 50 | 1.1777 | 2.0793 | 0.8291 |
|
333 |
+
| 0.8824 | 60 | 1.1344 | - | - |
|
334 |
+
| 1.0294 | 70 | 1.0578 | - | - |
|
335 |
+
| 1.1765 | 80 | 1.0981 | - | - |
|
336 |
+
| 1.3235 | 90 | 1.1216 | - | - |
|
337 |
+
| 1.4706 | 100 | 1.0436 | 2.0826 | 0.8283 |
|
338 |
+
| 1.6176 | 110 | 1.0422 | - | - |
|
339 |
+
| 1.7647 | 120 | 1.0857 | - | - |
|
340 |
+
| 1.9118 | 130 | 1.0502 | - | - |
|
341 |
+
| 2.0588 | 140 | 1.0363 | - | - |
|
342 |
+
| 2.2059 | 150 | 1.081 | 2.0763 | 0.8316 |
|
343 |
+
| 2.3529 | 160 | 1.1764 | - | - |
|
344 |
+
| 2.5 | 170 | 1.0393 | - | - |
|
345 |
+
| 2.6471 | 180 | 0.9586 | - | - |
|
346 |
+
| 2.7941 | 190 | 1.0537 | - | - |
|
347 |
+
| 2.9412 | 200 | 1.0313 | 2.0645 | 0.8325 |
|
348 |
+
| 3.0882 | 210 | 1.0401 | - | - |
|
349 |
+
| 3.2353 | 220 | 1.0389 | - | - |
|
350 |
+
| 3.3824 | 230 | 1.0225 | - | - |
|
351 |
+
| 3.5294 | 240 | 1.0131 | - | - |
|
352 |
+
| 3.6765 | 250 | 0.9565 | 2.0705 | 0.8308 |
|
353 |
+
| 3.8235 | 260 | 1.0059 | - | - |
|
354 |
+
| 3.9706 | 270 | 0.9629 | - | - |
|
355 |
+
| 4.1176 | 280 | 0.9546 | - | - |
|
356 |
+
| 4.2647 | 290 | 0.989 | - | - |
|
357 |
+
| 4.4118 | 300 | 1.0573 | 2.0514 | 0.8375 |
|
358 |
+
| 4.5588 | 310 | 0.894 | - | - |
|
359 |
+
| 4.7059 | 320 | 1.0082 | - | - |
|
360 |
+
| 4.8529 | 330 | 0.969 | - | - |
|
361 |
+
| 5.0 | 340 | 0.9187 | - | - |
|
362 |
+
| 5.1471 | 350 | 0.9034 | 2.0663 | 0.8350 |
|
363 |
+
| 5.2941 | 360 | 0.9043 | - | - |
|
364 |
+
| 5.4412 | 370 | 0.9517 | - | - |
|
365 |
+
| 5.5882 | 380 | 1.0272 | - | - |
|
366 |
+
| 5.7353 | 390 | 0.95 | - | - |
|
367 |
+
| 5.8824 | 400 | 0.8288 | 2.0400 | 0.8367 |
|
368 |
+
| 6.0294 | 410 | 0.9809 | - | - |
|
369 |
+
| 6.1765 | 420 | 0.8776 | - | - |
|
370 |
+
| 6.3235 | 430 | 0.9744 | - | - |
|
371 |
+
| 6.4706 | 440 | 0.9982 | - | - |
|
372 |
+
| 6.6176 | 450 | 0.9076 | 2.0429 | 0.8350 |
|
373 |
+
| 6.7647 | 460 | 0.8792 | - | - |
|
374 |
+
| 6.9118 | 470 | 0.787 | - | - |
|
375 |
+
| 7.0588 | 480 | 0.9506 | - | - |
|
376 |
+
| 7.2059 | 490 | 0.927 | - | - |
|
377 |
+
| 7.3529 | 500 | 0.9464 | 2.0487 | 0.8316 |
|
378 |
+
| 7.5 | 510 | 0.886 | - | - |
|
379 |
+
| 7.6471 | 520 | 0.9142 | - | - |
|
380 |
+
| 7.7941 | 530 | 0.8741 | - | - |
|
381 |
+
| 7.9412 | 540 | 0.8703 | - | - |
|
382 |
+
| 8.0882 | 550 | 0.8947 | 2.0411 | 0.8333 |
|
383 |
+
| 8.2353 | 560 | 0.8742 | - | - |
|
384 |
+
| 8.3824 | 570 | 0.8083 | - | - |
|
385 |
+
| 8.5294 | 580 | 0.9134 | - | - |
|
386 |
+
| 8.6765 | 590 | 0.8197 | - | - |
|
387 |
+
| 8.8235 | 600 | 0.8253 | 2.0272 | 0.8367 |
|
388 |
+
| 8.9706 | 610 | 0.8665 | - | - |
|
389 |
+
| 9.1176 | 620 | 0.8853 | - | - |
|
390 |
+
| 9.2647 | 630 | 0.7566 | - | - |
|
391 |
+
| 9.4118 | 640 | 0.9101 | - | - |
|
392 |
+
| 9.5588 | 650 | 0.801 | 2.0243 | 0.8350 |
|
393 |
+
| 9.7059 | 660 | 0.8551 | - | - |
|
394 |
+
| 9.8529 | 670 | 0.8748 | - | - |
|
395 |
+
| 10.0 | 680 | 0.9798 | - | - |
|
396 |
+
| 10.1471 | 690 | 1.0544 | - | - |
|
397 |
+
| 10.2941 | 700 | 1.2077 | 2.0128 | 0.8367 |
|
398 |
+
| 10.4412 | 710 | 1.0386 | - | - |
|
399 |
+
| 10.5882 | 720 | 1.0508 | - | - |
|
400 |
+
| 10.7353 | 730 | 1.0063 | - | - |
|
401 |
+
| 10.8824 | 740 | 1.0758 | - | - |
|
402 |
+
| 11.0294 | 750 | 1.1552 | 2.0031 | 0.8367 |
|
403 |
+
| 11.1765 | 760 | 1.0259 | - | - |
|
404 |
+
| 11.3235 | 770 | 1.0724 | - | - |
|
405 |
+
| 11.4706 | 780 | 1.0524 | - | - |
|
406 |
+
| 11.6176 | 790 | 0.9957 | - | - |
|
407 |
+
| 11.7647 | 800 | 1.0697 | 2.0022 | 0.8367 |
|
408 |
+
| 11.9118 | 810 | 1.0544 | - | - |
|
409 |
+
| 12.0588 | 820 | 1.0762 | - | - |
|
410 |
+
| 12.2059 | 830 | 1.0858 | - | - |
|
411 |
+
| 12.3529 | 840 | 1.0418 | - | - |
|
412 |
+
| 12.5 | 850 | 1.0041 | 1.9936 | 0.8392 |
|
413 |
+
| 12.6471 | 860 | 0.998 | - | - |
|
414 |
+
| 12.7941 | 870 | 1.0737 | - | - |
|
415 |
+
| 12.9412 | 880 | 1.0637 | - | - |
|
416 |
+
| 13.0882 | 890 | 0.9689 | - | - |
|
417 |
+
| 13.2353 | 900 | 1.001 | 1.9818 | 0.8392 |
|
418 |
+
| 13.3824 | 910 | 1.0418 | - | - |
|
419 |
+
| 13.5294 | 920 | 1.0097 | - | - |
|
420 |
+
| 13.6765 | 930 | 1.0244 | - | - |
|
421 |
+
| 13.8235 | 940 | 1.0383 | - | - |
|
422 |
+
| 13.9706 | 950 | 1.034 | 1.9798 | 0.8367 |
|
423 |
+
| 14.1176 | 960 | 0.9609 | - | - |
|
424 |
+
| 14.2647 | 970 | 1.049 | - | - |
|
425 |
+
| 14.4118 | 980 | 1.0012 | - | - |
|
426 |
+
| 14.5588 | 990 | 0.9008 | - | - |
|
427 |
+
| 14.7059 | 1000 | 1.0131 | 1.9741 | 0.8384 |
|
428 |
+
| 14.8529 | 1010 | 0.9714 | - | - |
|
429 |
+
| 15.0 | 1020 | 0.9987 | - | - |
|
430 |
+
| 15.1471 | 1030 | 1.1139 | - | - |
|
431 |
+
| 15.2941 | 1040 | 1.005 | - | - |
|
432 |
+
| 15.4412 | 1050 | 0.9074 | 1.9761 | 0.8359 |
|
433 |
+
| 15.5882 | 1060 | 0.9298 | - | - |
|
434 |
+
| 15.7353 | 1070 | 0.9335 | - | - |
|
435 |
+
| 15.8824 | 1080 | 0.9445 | - | - |
|
436 |
+
| 16.0294 | 1090 | 1.0087 | - | - |
|
437 |
+
| 16.1765 | 1100 | 0.9187 | 1.9679 | 0.8384 |
|
438 |
+
| 16.3235 | 1110 | 0.8502 | - | - |
|
439 |
+
| 16.4706 | 1120 | 0.9924 | - | - |
|
440 |
+
| 16.6176 | 1130 | 0.9982 | - | - |
|
441 |
+
| 16.7647 | 1140 | 0.9643 | - | - |
|
442 |
+
| 16.9118 | 1150 | 0.9491 | 1.9727 | 0.8333 |
|
443 |
+
| 17.0588 | 1160 | 0.9801 | - | - |
|
444 |
+
| 17.2059 | 1170 | 0.9374 | - | - |
|
445 |
+
| 17.3529 | 1180 | 0.8309 | - | - |
|
446 |
+
| 17.5 | 1190 | 0.9524 | - | - |
|
447 |
+
| 17.6471 | 1200 | 0.886 | 1.9797 | 0.8350 |
|
448 |
+
| 17.7941 | 1210 | 0.9026 | - | - |
|
449 |
+
| 17.9412 | 1220 | 0.8859 | - | - |
|
450 |
+
| 18.0882 | 1230 | 0.8745 | - | - |
|
451 |
+
| 18.2353 | 1240 | 0.9474 | - | - |
|
452 |
+
| 18.3824 | 1250 | 0.878 | 1.9737 | 0.8342 |
|
453 |
+
| 18.5294 | 1260 | 0.8372 | - | - |
|
454 |
+
| 18.6765 | 1270 | 0.833 | - | - |
|
455 |
+
| 18.8235 | 1280 | 0.9648 | - | - |
|
456 |
+
| 18.9706 | 1290 | 0.918 | - | - |
|
457 |
+
| 19.1176 | 1300 | 0.9588 | 1.9669 | 0.8359 |
|
458 |
+
| 19.2647 | 1310 | 1.0334 | - | - |
|
459 |
+
| 19.4118 | 1320 | 0.8347 | - | - |
|
460 |
+
| 19.5588 | 1330 | 0.828 | - | - |
|
461 |
+
| 19.7059 | 1340 | 0.9117 | - | - |
|
462 |
+
| 19.8529 | 1350 | 0.9123 | 1.9666 | 0.8350 |
|
463 |
+
| 20.0 | 1360 | 0.8538 | - | - |
|
464 |
+
|
465 |
+
</details>
|
466 |
+
|
467 |
+
### Framework Versions
|
468 |
+
- Python: 3.8.18
|
469 |
+
- Sentence Transformers: 3.1.1
|
470 |
+
- Transformers: 4.45.0
|
471 |
+
- PyTorch: 1.13.1+cu117
|
472 |
+
- Accelerate: 0.34.2
|
473 |
+
- Datasets: 3.0.1
|
474 |
+
- Tokenizers: 0.20.0
|
475 |
+
|
476 |
+
## Citation
|
477 |
+
|
478 |
+
### BibTeX
|
479 |
+
|
480 |
+
#### Sentence Transformers
|
481 |
+
```bibtex
|
482 |
+
@inproceedings{reimers-2019-sentence-bert,
|
483 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
484 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
485 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
486 |
+
month = "11",
|
487 |
+
year = "2019",
|
488 |
+
publisher = "Association for Computational Linguistics",
|
489 |
+
url = "https://arxiv.org/abs/1908.10084",
|
490 |
+
}
|
491 |
+
```
|
492 |
+
|
493 |
+
#### TripletLoss
|
494 |
+
```bibtex
|
495 |
+
@misc{hermans2017defense,
|
496 |
+
title={In Defense of the Triplet Loss for Person Re-Identification},
|
497 |
+
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
|
498 |
+
year={2017},
|
499 |
+
eprint={1703.07737},
|
500 |
+
archivePrefix={arXiv},
|
501 |
+
primaryClass={cs.CV}
|
502 |
+
}
|
503 |
+
```
|
504 |
+
|
505 |
+
<!--
|
506 |
+
## Glossary
|
507 |
+
|
508 |
+
*Clearly define terms in order to be accessible across audiences.*
|
509 |
+
-->
|
510 |
+
|
511 |
+
<!--
|
512 |
+
## Model Card Authors
|
513 |
+
|
514 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
515 |
+
-->
|
516 |
+
|
517 |
+
<!--
|
518 |
+
## Model Card Contact
|
519 |
+
|
520 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
521 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "dbourget/philai-embeddings-2.0",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"classifier_dropout": null,
|
8 |
+
"hidden_act": "gelu",
|
9 |
+
"hidden_dropout_prob": 0.1,
|
10 |
+
"hidden_size": 768,
|
11 |
+
"initializer_range": 0.02,
|
12 |
+
"intermediate_size": 3072,
|
13 |
+
"layer_norm_eps": 1e-12,
|
14 |
+
"max_position_embeddings": 512,
|
15 |
+
"model_type": "bert",
|
16 |
+
"num_attention_heads": 12,
|
17 |
+
"num_hidden_layers": 12,
|
18 |
+
"pad_token_id": 0,
|
19 |
+
"position_embedding_type": "absolute",
|
20 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.45.0",
|
23 |
+
"type_vocab_size": 2,
|
24 |
+
"use_cache": true,
|
25 |
+
"vocab_size": 30522
|
26 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "3.1.1",
|
4 |
+
"transformers": "4.45.0",
|
5 |
+
"pytorch": "1.13.1+cu117"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null,
|
9 |
+
"similarity_fn_name": null
|
10 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6923faf360cbe09177d3a84a6db49040707de78ef52a22a0b76b54740463f255
|
3 |
+
size 437951328
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"[PAD]",
|
4 |
+
"[UNK]",
|
5 |
+
"[CLS]",
|
6 |
+
"[SEP]",
|
7 |
+
"[MASK]"
|
8 |
+
],
|
9 |
+
"cls_token": {
|
10 |
+
"content": "[CLS]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"mask_token": {
|
17 |
+
"content": "[MASK]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"pad_token": {
|
24 |
+
"content": "[PAD]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"sep_token": {
|
31 |
+
"content": "[SEP]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"unk_token": {
|
38 |
+
"content": "[UNK]",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false
|
43 |
+
}
|
44 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[PAD]",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "[UNK]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"3": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"4": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"additional_special_tokens": [
|
45 |
+
"[PAD]",
|
46 |
+
"[UNK]",
|
47 |
+
"[CLS]",
|
48 |
+
"[SEP]",
|
49 |
+
"[MASK]"
|
50 |
+
],
|
51 |
+
"clean_up_tokenization_spaces": true,
|
52 |
+
"cls_token": "[CLS]",
|
53 |
+
"mask_token": "[MASK]",
|
54 |
+
"max_length": 512,
|
55 |
+
"model_max_length": 512,
|
56 |
+
"pad_to_multiple_of": null,
|
57 |
+
"pad_token": "[PAD]",
|
58 |
+
"pad_token_type_id": 0,
|
59 |
+
"padding_side": "right",
|
60 |
+
"sep_token": "[SEP]",
|
61 |
+
"stride": 0,
|
62 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
63 |
+
"truncation_side": "right",
|
64 |
+
"truncation_strategy": "longest_first",
|
65 |
+
"unk_token": "[UNK]"
|
66 |
+
}
|