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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-2586365/trainer_state.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+  "word_embedding_dimension": 128,
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+  "pooling_mode_mean_tokens": true,
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README.md

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+
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- autotrain
+base_model: cnmoro/micro-bertim
+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:
+- cnmoro/AllTripletsMsMarco-PTBR
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Sentence Transformers
+
+## Validation Metrics
+loss: 0.3181200921535492
+
+cosine_accuracy: 0.8921948650328134
+
+runtime: 2141.8089
+
+samples_per_second: 246.442
+
+steps_per_second: 12.322
+
+: 1.0
+
+## 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-2586365/1_Pooling/config.json

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checkpoint-2586365/config.json

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+}

checkpoint-2586365/config_sentence_transformers.json

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+  "similarity_fn_name": "cosine"
+}

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config.json

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sentence_bert_config.json

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training_params.json

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+}