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--- |
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language: [] |
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library_name: sentence-transformers |
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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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- dataset_size:100K<n<1M |
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- loss:CoSENTLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: Frequent headaches and muscle soreness are a result of my insomnia. |
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sentences: |
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- My frequent headaches and muscle soreness are a direct result of my insomnia. |
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- A manic episode often prevents me from sitting still or relaxing as I constantly |
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need to be on the move. |
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- The fear of being away from familiar places during a panic attack is why I have |
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refused job opportunities with travel obligations. |
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- source_sentence: My insomnia results in frequent headaches and muscle soreness for |
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me. |
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sentences: |
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- Due to my insomnia, I have frequent headaches and muscle soreness. |
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- Thoughts of life not being worth living and feelings of hopelessness create a |
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difficult challenge for me. |
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- The fear of being away from familiar places during a panic attack is why I have |
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refused job opportunities with travel obligations. |
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- source_sentence: Faced with a snake, fear takes over and I stay frozen until it |
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passes. |
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sentences: |
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- Whenever I encounter a snake, I freeze in fear and cannot move until it is gone. |
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- Due to a sense of unworthiness of happiness, I struggle to enjoy activities that |
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were once my favorites. |
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- The fear of being away from familiar places during a panic attack is why I have |
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refused job opportunities with travel obligations. |
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- source_sentence: The idea of overdosing on medication crosses my mind when overwhelmed. |
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sentences: |
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- Thoughts of overdosing on medication often occur to me when I'm overwhelmed. |
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- I, almost like being stuck in a loop, repeat certain actions or words without |
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any clear purpose at times. |
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- The fear of being away from familiar places during a panic attack is why I have |
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refused job opportunities with travel obligations. |
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- source_sentence: Insomnia has led me to experience frequent headaches and muscle |
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soreness. |
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sentences: |
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- My insomnia has caused me to experience frequent headaches and muscle soreness. |
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- I struggle with distinguishing between reality and illusions when I feel detached |
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from reality at times. |
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- The fear of being away from familiar places during a panic attack is why I have |
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refused job opportunities with travel obligations. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: FT label aug |
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type: FT_label_aug |
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metrics: |
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- type: pearson_cosine |
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value: 0.42561450628852554 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.23253817395631948 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.5095430319125491 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.23187290173483613 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.5153981915417447 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.232538168642362 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.4256145064012167 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.23253817993475548 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.5153981915417447 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.23253817993475548 |
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name: Spearman Max |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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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("Hgkang00/FT-label-aug-consent-10") |
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# Run inference |
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sentences = [ |
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'Insomnia has led me to experience frequent headaches and muscle soreness.', |
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'My insomnia has caused me to experience frequent headaches and muscle soreness.', |
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'I struggle with distinguishing between reality and illusions when I feel detached from reality at times.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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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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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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### Out-of-Scope Use |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `FT_label_aug` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.4256 | |
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| **spearman_cosine** | **0.2325** | |
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| pearson_manhattan | 0.5095 | |
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| spearman_manhattan | 0.2319 | |
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| pearson_euclidean | 0.5154 | |
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| spearman_euclidean | 0.2325 | |
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| pearson_dot | 0.4256 | |
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| spearman_dot | 0.2325 | |
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| pearson_max | 0.5154 | |
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| spearman_max | 0.2325 | |
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## Bias, Risks and Limitations |
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 133,800 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 11 tokens</li><li>mean: 31.63 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 25.22 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: -1.0</li><li>mean: -0.92</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| |
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| <code>Presence of one or more of the following intrusion symptoms associated with the traumatic event: recurrent distressing memories, dreams, flashbacks, psychological distress, or physiological reactions to cues of the traumatic event.</code> | <code>I avoid making phone calls, even to close friends or family, because I'm afraid of saying something wrong or sounding awkward.</code> | <code>0.0</code> | |
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| <code>The phobic object or situation almost always provokes immediate fear or anxiety.</code> | <code>I find it hard to stick to a consistent eating schedule, sometimes going days without feeling the need to eat at all.</code> | <code>-1.0</code> | |
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| <code>The fear or anxiety is out of proportion to the actual danger posed by the specific object or situation and to the sociocultural context.</code> | <code>I have difficulty going to places where I feel there are no immediate exits, such as cinemas or auditoriums, as the fear of being stuck or unable to escape escalates my anxiety.</code> | <code>-1.0</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 104,225 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 11 tokens</li><li>mean: 31.24 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 24.86 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: -1.0</li><li>mean: -0.93</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|:------------------| |
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| <code>Excessive anxiety and worry occurring more days than not for at least 6 months, about a number of events or activities such as work or school performance.</code> | <code>Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.</code> | <code>-1.0</code> | |
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| <code>The individual fears acting in a way or showing anxiety symptoms that will be negatively evaluated, leading to humiliation, embarrassment, rejection, or offense to others.</code> | <code>I often find myself mindlessly snacking throughout the day due to changes in my appetite.</code> | <code>-1.0</code> | |
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| <code>Persistent avoidance of stimuli associated with the trauma, evidenced by avoiding distressing memories, thoughts, or feelings, or external reminders of the event.</code> | <code>Simple activities like going for a walk or doing household chores feel like daunting tasks due to my low energy levels.</code> | <code>-1.0</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 128 |
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- `num_train_epochs`: 10 |
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- `warmup_ratio`: 0.1 |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 128 |
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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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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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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`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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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 |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | loss | FT_label_aug_spearman_cosine | |
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|:------:|:----:|:-------------:|:-------:|:----------------------------:| |
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| 1.0 | 523 | 7.773 | - | - | |
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| 2.0 | 1046 | 0.0004 | - | - | |
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| 2.9828 | 1560 | - | 11.8818 | 0.2184 | |
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| 1.0172 | 1569 | 0.1169 | - | - | |
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| 2.0172 | 2092 | 5.4076 | - | - | |
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| 3.0172 | 2615 | 0.0002 | - | - | |
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| 3.9828 | 3120 | - | 11.8669 | 0.2054 | |
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| 2.0344 | 3138 | 0.1571 | - | - | |
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| 3.0344 | 3661 | 4.0179 | - | - | |
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| 4.0344 | 4184 | 0.0001 | - | - | |
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| 4.9828 | 4680 | - | 12.8814 | 0.2291 | |
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| 3.0516 | 4707 | 0.1592 | - | - | |
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| 4.0516 | 5230 | 2.835 | 13.5336 | 0.2325 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.0 |
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- Transformers: 4.41.1 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.30.1 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### CoSENTLoss |
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```bibtex |
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@online{kexuefm-8847, |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
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author={Su Jianlin}, |
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year={2022}, |
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month={Jan}, |
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url={https://kexue.fm/archives/8847}, |
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} |
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``` |
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