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makiart
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ft-modern-bert-emb-all-nli

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
makiart commited on
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eed4a1a
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1 Parent(s): 7d44deb

Add new SentenceTransformer model

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Files changed (10) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +437 -0
  3. config.json +46 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +945 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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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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+ }
README.md ADDED
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+ ---
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+ language:
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+ - en
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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:557850
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: answerdotai/ModernBERT-base
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+ widget:
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+ - source_sentence: A man is jumping unto his filthy bed.
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+ sentences:
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+ - A young male is looking at a newspaper while 2 females walks past him.
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+ - The bed is dirty.
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+ - The man is on the moon.
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+ - source_sentence: A carefully balanced male stands on one foot near a clean ocean
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+ beach area.
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+ sentences:
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+ - A man is ouside near the beach.
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+ - Three policemen patrol the streets on bikes
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+ - A man is sitting on his couch.
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+ - source_sentence: The man is wearing a blue shirt.
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+ sentences:
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+ - Near the trashcan the man stood and smoked
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+ - A man in a blue shirt leans on a wall beside a road with a blue van and red car
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+ with water in the background.
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+ - A man in a black shirt is playing a guitar.
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+ - source_sentence: The girls are outdoors.
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+ sentences:
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+ - Two girls riding on an amusement part ride.
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+ - a guy laughs while doing laundry
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+ - Three girls are standing together in a room, one is listening, one is writing
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+ on a wall and the third is talking to them.
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+ - source_sentence: A construction worker peeking out of a manhole while his coworker
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+ sits on the sidewalk smiling.
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+ sentences:
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+ - A worker is looking out of a manhole.
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+ - A man is giving a presentation.
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+ - The workers are both inside the manhole.
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+ datasets:
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+ - sentence-transformers/all-nli
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy
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+ model-index:
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+ - name: SentenceTransformer based on answerdotai/ModernBERT-base
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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: all nli dev
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+ type: all-nli-dev
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.8494835965978129
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+ name: Cosine Accuracy
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+ ---
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+
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+ # SentenceTransformer based on answerdotai/ModernBERT-base
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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.
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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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 5756c58a31a2478f9e62146021f48295a92c3da5 -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
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+ - **Language:** en
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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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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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
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+ (1): Pooling({'word_embedding_dimension': 768, '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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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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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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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("makiart/ft-modern-bert-emb-all-nli")
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+ # Run inference
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+ sentences = [
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+ 'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
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+ 'A worker is looking out of a manhole.',
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+ 'The workers are both inside the manhole.',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 768]
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+
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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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+ <!--
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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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+
140
+ <details><summary>Click to expand</summary>
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+
142
+ </details>
143
+ -->
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+
145
+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ -->
150
+
151
+ ## Evaluation
152
+
153
+ ### Metrics
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+
155
+ #### Triplet
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+
157
+ * Dataset: `all-nli-dev`
158
+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | **cosine_accuracy** | **0.8495** |
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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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+
170
+ <!--
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+ ### Recommendations
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+
173
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
176
+ ## Training Details
177
+
178
+ ### Training Dataset
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+
180
+ #### all-nli
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+
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+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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+ * Size: 557,850 training samples
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+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive | negative |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.91 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.49 tokens</li><li>max: 51 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
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+ | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
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+ | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
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+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
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+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) 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": "cos_sim"
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+ }
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+ ```
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+
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+ ### Evaluation Dataset
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+
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+ #### all-nli
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+
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+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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+ * Size: 6,584 evaluation samples
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+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive | negative |
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+ |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.88 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.48 tokens</li><li>max: 29 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
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+ | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
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+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
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+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
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+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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+ ```json
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+ {
225
+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
227
+ }
228
+ ```
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+
230
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `batch_sampler`: no_duplicates
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
243
+
244
+ - `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`: 64
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+ - `per_device_eval_batch_size`: 64
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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`: 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`: 1
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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`: True
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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`: None
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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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+ - `include_for_metrics`: []
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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`:
337
+ - `auto_find_batch_size`: False
338
+ - `full_determinism`: False
339
+ - `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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+ - `eval_on_start`: False
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+ - `use_liger_kernel`: False
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+ - `eval_use_gather_object`: False
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+ - `average_tokens_across_devices`: False
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+ - `prompts`: None
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+ - `batch_sampler`: no_duplicates
358
+ - `multi_dataset_batch_sampler`: proportional
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+
360
+ </details>
361
+
362
+ ### Training Logs
363
+ | Epoch | Step | Training Loss | Validation Loss | all-nli-dev_cosine_accuracy |
364
+ |:------:|:----:|:-------------:|:---------------:|:---------------------------:|
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+ | 0 | 0 | - | - | 0.6536 |
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+ | 0.0640 | 100 | 2.7064 | 1.5341 | 0.7707 |
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+ | 0.1280 | 200 | 1.0936 | 1.3577 | 0.7860 |
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+ | 0.1919 | 300 | 1.0731 | 1.3709 | 0.7810 |
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+ | 0.2559 | 400 | 0.8955 | 1.3670 | 0.7836 |
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+ | 0.3199 | 500 | 0.8771 | 1.3368 | 0.7778 |
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+ | 0.3839 | 600 | 1.0064 | 1.3147 | 0.7965 |
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+ | 0.4479 | 700 | 1.1011 | 1.1929 | 0.8077 |
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+ | 0.5118 | 800 | 0.8893 | 1.1237 | 0.8150 |
374
+ | 0.5758 | 900 | 1.1223 | 1.0457 | 0.8411 |
375
+ | 0.6398 | 1000 | 0.9564 | 1.0189 | 0.8477 |
376
+ | 0.7038 | 1100 | 1.0213 | 1.0453 | 0.8351 |
377
+ | 0.7678 | 1200 | 1.0531 | 1.0329 | 0.8395 |
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+ | 0.8317 | 1300 | 0.9353 | 1.0105 | 0.8414 |
379
+ | 0.8957 | 1400 | 1.0907 | 1.0097 | 0.8452 |
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+ | 0.9597 | 1500 | 0.2253 | 1.0132 | 0.8495 |
381
+
382
+
383
+ ### Framework Versions
384
+ - Python: 3.10.12
385
+ - Sentence Transformers: 3.3.1
386
+ - Transformers: 4.48.0.dev0
387
+ - PyTorch: 2.5.1+cu121
388
+ - Accelerate: 1.2.1
389
+ - Datasets: 3.2.0
390
+ - Tokenizers: 0.21.0
391
+
392
+ ## Citation
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+
394
+ ### BibTeX
395
+
396
+ #### Sentence Transformers
397
+ ```bibtex
398
+ @inproceedings{reimers-2019-sentence-bert,
399
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
400
+ author = "Reimers, Nils and Gurevych, Iryna",
401
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
402
+ month = "11",
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+ year = "2019",
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+ publisher = "Association for Computational Linguistics",
405
+ url = "https://arxiv.org/abs/1908.10084",
406
+ }
407
+ ```
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+
409
+ #### MultipleNegativesRankingLoss
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+ ```bibtex
411
+ @misc{henderson2017efficient,
412
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
413
+ 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},
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+ year={2017},
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+ eprint={1705.00652},
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+ archivePrefix={arXiv},
417
+ primaryClass={cs.CL}
418
+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
425
+ -->
426
+
427
+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
431
+ -->
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+
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+ <!--
434
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