nreimers commited on
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CECorrelationEvaluator_sts-dev_results.csv ADDED
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+ epoch,steps,Pearson_Correlation,Spearman_Correlation
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+ 0,-1,0.868755228331292,0.8660826790688932
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+ 1,-1,0.8826808896987867,0.8779567515610578
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+ 2,-1,0.8842270653327202,0.8775979797054484
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+ 3,-1,0.8850764187076714,0.8791573934188543
README.md ADDED
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+ # Cross-Encoder for Quora Duplicate Questions Detection
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+ This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
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+
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+ ## Training Data
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+ 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.
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+
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+
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+ ## Usage and Performance
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+
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+ Pre-trained models can be used like this:
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+ ```
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+ from sentence_transformers import CrossEncoder
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+ model = CrossEncoder('model_name')
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+ scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
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+ ```
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+
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+ The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
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+
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+ You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
config.json ADDED
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+ {
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+ "architectures": [
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+ "RobertaForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "eos_token_id": 2,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "LABEL_0": 0
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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+ "pad_token_id": 1,
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+ "type_vocab_size": 1,
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+ "vocab_size": 50265
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+ }
merges.txt ADDED
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pytorch_model.bin ADDED
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special_tokens_map.json ADDED
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+ {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}
tokenizer_config.json ADDED
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+ {"model_max_length": 512, "special_tokens_map_file": "final-models/ce-distilroberta-base-mnli/special_tokens_map.json", "full_tokenizer_file": null}
vocab.json ADDED
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