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add model
Browse files- .ipynb_checkpoints/README-checkpoint.md +33 -0
- config.json +2 -2
- pytorch_model.bin +2 -2
.ipynb_checkpoints/README-checkpoint.md
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---
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pipeline_tag: text-classification
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tags:
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- natural-language-inference
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- misogyny
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language: en
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license: apache-2.0
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widget:
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- text: "Las mascarillas causan hipoxia. Wearing masks is harmful to human health"
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example_title: "Natural Language Inference"
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---
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# bertweet-base-multi-mami
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This is a finetuned XLM-RoBERTA model for natural language inference. It has been trained with a massive ammount of data following the ANLI pipeline training. We include data from:
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- [mnli](https://cims.nyu.edu/~sbowman/multinli/) {train, dev and test}
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- [snli](https://nlp.stanford.edu/projects/snli/) {train, dev and test}
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- [xnli](https://github.com/facebookresearch/XNLI) {train, dev and test}
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- [fever](https://fever.ai/resources.html) {train, dev and test}
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- [anli](https://github.com/facebookresearch/anli) {train}
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The model is validated on ANLI training sets, including R1, R2 and R3. The following results can be expected on the testing splits.
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|Split|Accuracy|
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|-|-|
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|R1|0.6610|
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|R2|0.4990|
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|R3|0.4425|
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# Multilabels
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label2id={
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"contradiction": 0,
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"entailment": 1,
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"neutral": 2,
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},
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config.json
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{
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"_name_or_path": "xlm-roberta-large",
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"architectures": [
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"XLMRobertaForSequenceClassification"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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{
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"_name_or_path": "xlm-roberta-large-snli_mnli_xnli_fever_r1_r2_r3",
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"architectures": [
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"XLMRobertaForSequenceClassification"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.12.3",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:b7cce651115ecbb97dc077f548c5e3d0183737e3f88bbf55275b9ac69efd0c1d
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size 2239732653
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