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Model: cross-encoder/stsb-TinyBERT-L4
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+datasets:
+- sentence-transformers/stsb
+language:
+- en
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+tags:
+- transformers
+---
+# Cross-Encoder for Semantic Textual Similarity
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
+
+## Training Data
+This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 
+
+
+## Usage and Performance
+
+Pre-trained models can be used like this:
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder('cross-encoder/stsb-TinyBERT-L4')
+scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
+```
+
+The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
+
+You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class

config.json

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+  "_name_or_path": "output/TinyBERT_L-4-nli/",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
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+  "hidden_act": "gelu",
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+  "label2id": {
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "type_vocab_size": 2,
+  "vocab_size": 30522
+}

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tokenizer_config.json

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+}