lrodrigues
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Browse files- .gitattributes +9 -8
- CEBinaryClassificationEvaluator_Quora-dev_results.csv +15 -0
- README.md +36 -0
- config.json +28 -0
- flax_model.msgpack +3 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +51 -0
- tokenizer_config.json +6 -0
- vocab.json +0 -0
.gitattributes
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CEBinaryClassificationEvaluator_Quora-dev_results.csv
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README.md
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---
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datasets:
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- quora
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language: en
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license: mit
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pipeline_tag: text-classification
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tags:
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- roberta
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- text-classification
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---
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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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This model uses [roberta-base](https://huggingface.co/roberta-base).
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## Training Data
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This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset.
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The model will predict a score between 0 and 1: How likely the two given questions are duplicates.
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Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
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## Usage and Performance
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The trained model can be used like this:
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```python
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from sentence_transformers import CrossEncoder
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model = CrossEncoder('model_name')
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scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
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print(scores)
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```
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config.json
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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": 12,
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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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}
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flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:a7e3f35ee5fa4bbd55ddee08e12bdbe19fcec65d978d5df08bccacd206964e4f
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size 498592825
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 498676169
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special_tokens_map.json
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}
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}
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tokenizer_config.json
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}
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vocab.json
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