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README.md

@@ -1,3 +1,129 @@
 ---
+pipeline_tag: text-classification
+
+language:
+
+- ca
+
 license: apache-2.0
+
+tags:
+
+- "catalan"
+
+- "semantic textual similarity"
+
+- "sts-ca"
+
+- "CaText"
+
+- "Catalan Textual Corpus"
+
+datasets:
+
+- "projecte-aina/sts-ca"
+
+metrics:
+
+- "combined_score"
+
+model-index:
+
+- name: roberta-base-ca-v2-cased-sts
+  results:
+  - task: 
+      type: text-classification
+    dataset:
+      type:   projecte-aina/sts-ca
+      name: STS-ca
+    metrics:
+      - name: Combined score
+        type: combined_score
+        value: 0.7907
+        
 ---
+
+# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Semantic Textual Similarity.
+
+The **roberta-base-ca-v2-cased-sts** is a Semantic Textual Similarity (STS) 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).
+
+## Datasets
+We used the STS dataset in Catalan called [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) for training and evaluation.
+
+## Evaluation and results
+We evaluated the _roberta-base-ca-v2-cased-sts_ on the STS-ca test set against standard multilingual and monolingual baselines:
+
+| Model       | STS-ca (Combined score)   | 
+| ------------|:-------------|
+| roberta-base-ca-v2-cased-sts | 79.07 |
+| roberta-base-ca-cased-sts | **80.19** |
+| mBERT       | 74.26 |
+| XLM-RoBERTa | 61.61 |
+
+
+
+For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
+
+## How to use
+To get the correct<sup>1</sup> model's prediction scores with values between 0.0 and 5.0, use the following code:
+
+```python
+from transformers import pipeline, AutoTokenizer
+from scipy.special import logit
+
+model = 'projecte-aina/roberta-base-ca-v2-cased-sts'
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipe = pipeline('text-classification', model=model, tokenizer=tokenizer)
+
+def prepare(sentence_pairs):
+    sentence_pairs_prep = []
+    for s1, s2 in sentence_pairs:
+        sentence_pairs_prep.append(f"{tokenizer.cls_token} {s1}{tokenizer.sep_token}{tokenizer.sep_token} {s2}{tokenizer.sep_token}")
+    return sentence_pairs_prep
+
+sentence_pairs = [("El llibre va caure per la finestra.", "El llibre va sortir volant."),
+                  ("M'agrades.", "T'estimo."),
+                  ("M'agrada el sol i la calor", "A la Garrotxa plou molt.")]
+
+predictions = pipe(prepare(sentence_pairs), add_special_tokens=False)
+
+# convert back to scores to the original 1 and 5 interval
+for prediction in predictions:
+    prediction['score'] = logit(prediction['score'])
+print(predictions)
+```
+Expected output:
+```
+[{'label': 'LABEL_0', 'score': 2.2975975792221486}, 
+{'label': 'LABEL_0', 'score': 2.4812691815397376}, 
+{'label': 'LABEL_0', 'score': 1.090076983490159}]
+```
+
+<sup>1</sup> _**avoid using the widget** scores since they are normalized and do not reflect the original annotation values._
+
+## Citing 
+If you use any of these resources (datasets or models) in your work, please cite our latest paper:
+```bibtex
+@inproceedings{armengol-estape-etal-2021-multilingual,
+    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
+    author = "Armengol-Estap{\'e}, Jordi  and
+      Carrino, Casimiro Pio  and
+      Rodriguez-Penagos, Carlos  and
+      de Gibert Bonet, Ona  and
+      Armentano-Oller, Carme  and
+      Gonzalez-Agirre, Aitor  and
+      Melero, Maite  and
+      Villegas, Marta",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.437",
+    doi = "10.18653/v1/2021.findings-acl.437",
+    pages = "4933--4946",
+}
+```
+
+### Funding
+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).

config.json

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+{
+  "_name_or_path": "projecte-aina/roberta-base-ca-v2-cased-sts",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "finetuning_task": "stsb",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "SIMILARITY"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "SIMILARITY": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "regression",
+  "transformers_version": "4.6.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50262
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:975baf008a16b632ff4df7c422bd26f02b3ba49291552521ab482a6f837ac1d8
+size 498661805

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}}

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"}