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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 128,
+  "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,
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+}

README.md

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+
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- autotrain
+base_model: google/bert_uncased_L-2_H-128_A-2
+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/PremiseHypothesisLabel_ENPT
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Sentence Transformers
+
+## Validation Metrics
+loss: 0.056979671120643616
+
+runtime: 118.8683
+
+samples_per_second: 2984.379
+
+steps_per_second: 5.83
+
+: 5.0
+
+## 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-339505/1_Pooling/config.json

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+}

checkpoint-339505/config.json

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+}

checkpoint-339505/config_sentence_transformers.json

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+    "pytorch": "2.5.1"
+  },
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+  "similarity_fn_name": "cosine"
+}

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checkpoint-339505/sentence_bert_config.json

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checkpoint-339505/special_tokens_map.json

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checkpoint-339505/tokenizer_config.json

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config.json

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config_sentence_transformers.json

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runs/Dec24_10-38-00_Moro/events.out.tfevents.1735047481.Moro.2829759.0

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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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+    "data_path": "cnmoro/PremiseHypothesisLabel_ENPT",
+    "model": "google/bert_uncased_L-2_H-128_A-2",
+    "lr": 2e-05,
+    "epochs": 5,
+    "max_seq_length": 256,
+    "batch_size": 256,
+    "warmup_ratio": 0.1,
+    "gradient_accumulation": 1,
+    "optimizer": "adamw_torch",
+    "scheduler": "linear",
+    "weight_decay": 0.0,
+    "max_grad_norm": 1.0,
+    "seed": 42,
+    "train_split": "train",
+    "valid_split": "test",
+    "logging_steps": -1,
+    "project_name": "autotrain-st-pair-class",
+    "auto_find_batch_size": false,
+    "mixed_precision": "fp16",
+    "save_total_limit": 1,
+    "push_to_hub": true,
+    "eval_strategy": "epoch",
+    "username": "cnmoro",
+    "log": "tensorboard",
+    "early_stopping_patience": 5,
+    "early_stopping_threshold": 0.01,
+    "trainer": "pair_class",
+    "sentence1_column": "premise",
+    "sentence2_column": "hypothesis",
+    "sentence3_column": null,
+    "target_column": "label"
+}