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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- autotrain
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: 'search_query: i love autotrain'
+  sentences:
+  - 'search_query: huggingface auto train'
+  - 'search_query: hugging face auto train'
+  - 'search_query: i love autotrain'
+pipeline_tag: sentence-similarity
+datasets:
+- sentence-transformers/all-nli
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Sentence Transformers
+
+## Validation Metrics
+loss: 0.20218822360038757
+
+cosine_accuracy: 0.9600546780072904
+
+runtime: 246.062
+
+samples_per_second: 26.757
+
+steps_per_second: 1.674
+
+: 3.0
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the Hugging Face Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'search_query: autotrain',
+    'search_query: auto train',
+    'search_query: i love autotrain',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+```

checkpoint-209196/1_Pooling/config.json

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+  "include_prompt": true
+}

checkpoint-209196/config.json

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+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
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+}

checkpoint-209196/config_sentence_transformers.json

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+  "__version__": {
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+    "transformers": "4.48.0",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
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+  "similarity_fn_name": "cosine"
+}

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checkpoint-209196/modules.json

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checkpoint-209196/sentence_bert_config.json

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checkpoint-209196/special_tokens_map.json

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config.json

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config_sentence_transformers.json

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sentence_bert_config.json

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special_tokens_map.json

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tokenizer_config.json

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training_params.json

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+    "model": "sentence-transformers/all-MiniLM-L6-v2",
+    "lr": 3e-05,
+    "epochs": 3,
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+    "log": "tensorboard",
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+    "early_stopping_threshold": 0.01,
+    "trainer": "triplet",
+    "sentence1_column": "anchor",
+    "sentence2_column": "positive",
+    "sentence3_column": "negative",
+    "target_column": "target"
+}