gonzalez-agirre
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Upload model.
Browse files- README.md +97 -0
- config.json +38 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.json +0 -0
README.md
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---
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license: apache-2.0
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---
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---
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language:
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- ca
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license: apache-2.0
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tags:
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- "catalan"
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- "textual entailment"
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- "teca"
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- "CaText"
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- "Catalan Textual Corpus"
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datasets:
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- "projecte-aina/teca"
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metrics:
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- "accuracy"
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model-index:
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- name: roberta-base-ca-v2-cased-te
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results:
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- task:
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type: text-classification # Required. Example: automatic-speech-recognition
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dataset:
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type: projecte-aina/teca
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name: TECA
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8342
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widget:
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- text: "M'agrades. T'estimo."
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- text: "M'agrada el sol i la calor. A la Garrotxa plou molt."
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- text: "El llibre va caure per la finestra. El llibre va sortir volant."
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- text: "El meu aniversari és el 23 de maig. Faré anys a finals de maig."
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---
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# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Textual Entailment.
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The **roberta-base-ca-v2-cased-te** is a Textual Entailment (TE) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details).
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## Datasets
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We used the TE dataset in Catalan called [TECA](https://huggingface.co/datasets/projecte-aina/teca) for training and evaluation.
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## Evaluation and results
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We evaluated the roberta-base-ca-cased-te on the TECA test set against standard multilingual and monolingual baselines:
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| Model | TECA (Accuracy) |
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| ------------|:----|
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| roberta-base-ca-v2-cased-te | **83.14** |
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| BERTa | 79.26 |
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| mBERT | 74.63 |
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| XLM-RoBERTa | 33.30 |
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For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
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## Citing
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If you use any of these resources (datasets or models) in your work, please cite our latest paper:
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```bibtex
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@inproceedings{armengol-estape-etal-2021-multilingual,
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title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
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author = "Armengol-Estap{\'e}, Jordi and
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Carrino, Casimiro Pio and
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Rodriguez-Penagos, Carlos and
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de Gibert Bonet, Ona and
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Armentano-Oller, Carme and
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Gonzalez-Agirre, Aitor and
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Melero, Maite and
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Villegas, Marta",
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
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month = aug,
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year = "2021",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2021.findings-acl.437",
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doi = "10.18653/v1/2021.findings-acl.437",
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pages = "4933--4946",
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}
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```
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### Funding
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This work was funded by the [Catalan Government](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of the [AINA project.](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
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config.json
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{
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"_name_or_path": "projecte-aina/roberta-base-ca-v2-cased-te",
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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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"finetuning_task": "mnli",
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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": "ENTAILMENT",
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"1": "NEUTRAL",
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"2": "CONTRADICTION"
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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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"ENTAILMENT": 0,
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"NEUTRAL": 1,
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"CONTRADICTION": 2
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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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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"transformers_version": "4.6.1",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50262
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}
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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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oid sha256:a06de16611b37b1379645a1849a9996dc983a11b72efd67fd4027245c24a9de7
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size 498667949
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special_tokens_map.json
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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}}
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tokenizer.json
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tokenizer_config.json
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{"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": true, "errors": "replace", "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "max_len": 512, "special_tokens_map_file": null, "name_or_path": "/gpfs/projects/bsc88/BERTs/models/roberta_base_ca_jsc/transformed_lr0.0005"}
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vocab.json
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