BSC-LT
/

roberta-base-ca

@@ -8,239 +8,19 @@ license: apache-2.0
 ---
 # BERTa: RoBERTa-based Catalan language model
-<span style="color:red"> WARNING: This repository is now superseded by </span> [BSC-TeMU/roberta-base-ca-cased](https://huggingface.co/BSC-TeMU/roberta-base-ca-cased). <span style="color:red"> Future updates will be released in the new repository, so it is highly recommended to load the model using the new path: </span>
 ```python
 from transformers import AutoModel
 model = AutoModel.from_pretrained("BSC-TeMU/roberta-base-ca-cased")
 ```
-<span style="color:red"> From now on, all models and datasets from the BSC's Text Mining Unit will be published on the </span> [official organization account](https://huggingface.co/BSC-TeMU).
-## BibTeX  citation
-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",
-}
-```
-## Model description
-BERTa is a transformer-based masked language model for the Catalan language.
-It is based on the [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) base model
-and has been trained on a medium-size corpus collected from publicly available corpora and crawlers.
-## Training corpora and preprocessing
-The training corpus consists of several corpora gathered from web crawling and public corpora.
-The publicly available corpora are:
- 1. the Catalan part of the [DOGC](http://opus.nlpl.eu/DOGC-v2.php) corpus, a set of documents from the Official Gazette of the Catalan Government
- 2. the [Catalan Open Subtitles](http://opus.nlpl.eu/download.php?f=OpenSubtitles/v2018/mono/OpenSubtitles.raw.ca.gz), a collection of translated movie subtitles
- 3. the non-shuffled version of the Catalan part of the [OSCAR](https://traces1.inria.fr/oscar/) corpus \\\\cite{suarez2019asynchronous},
-    a collection of monolingual corpora, filtered from [Common Crawl](https://commoncrawl.org/about/)
- 4. The [CaWac](http://nlp.ffzg.hr/resources/corpora/cawac/) corpus, a web corpus of Catalan built from the .cat top-level-domain in late 2013
-    the non-deduplicated version
- 5. the [Catalan Wikipedia articles](https://ftp.acc.umu.se/mirror/wikimedia.org/dumps/cawiki/20200801/) downloaded on 18-08-2020.
-The crawled corpora are:
- 6. The Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains
- 7. the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government
- 8. the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the [Catalan News Agency](https://www.acn.cat/)
-To obtain a high-quality training corpus, each corpus have preprocessed with a pipeline of operations, including among the others,
-sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents.
-During the process, we keep document boundaries are kept.
-Finally, the corpora are concatenated and further global deduplication among the corpora is applied.
-The final training corpus consists of about 1,8B tokens.
-## Tokenization and pretraining
-The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
-used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens.
-The BERTa pretraining consists of a masked language model training that follows the approach employed for the RoBERTa base model
-with the same hyperparameters as in the original work.
-The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM.
-## Evaluation
-## CLUB benchmark
-The BERTa model has been fine-tuned on the downstream tasks of the Catalan Language Understanding Evaluation benchmark (CLUB),
-that has been created along with the model.
-It contains the following tasks and their related datasets:
- 1. Part-of-Speech Tagging (POS)
-    Catalan-Ancora: from the [Universal Dependencies treebank](https://github.com/UniversalDependencies/UD_Catalan-AnCora) of the well-known Ancora corpus
- 2. Named Entity Recognition (NER)
-    **[AnCora Catalan 2.0.0](https://zenodo.org/record/4762031#.YKaFjqGxWUk)**: extracted named entities from the original [Ancora](https://doi.org/10.5281/zenodo.4762030) version,
-    filtering out some unconventional ones, like book titles, and transcribed them into a standard CONLL-IOB format
- 3. Text Classification (TC)
-    **[TeCla](https://doi.org/10.5281/zenodo.4627197)**: consisting of 137k news pieces from the Catalan News Agency ([ACN](https://www.acn.cat/)) corpus
- 4. Semantic Textual Similarity (STS)
-    **[Catalan semantic textual similarity](https://doi.org/10.5281/zenodo.4529183)**: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them,
-    scraped from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349)
- 5. Question Answering (QA):
-    **[ViquiQuAD](https://doi.org/10.5281/zenodo.4562344)**: consisting of more than 15,000 questions outsourced from Catalan Wikipedia randomly chosen from a set of 596 articles that were originally written in Catalan.
-    **[XQuAD](https://doi.org/10.5281/zenodo.4526223)**: the Catalan translation of XQuAD, a multilingual collection of manual translations of 1,190 question-answer pairs from English Wikipedia used only as a _test set_
-Here are the train/dev/test splits of the datasets:
-| Task (Dataset) | Total | Train | Dev  | Test |
-|:--|:--|:--|:--|:--|
-| NER (Ancora)  |13,581 | 10,628 | 1,427 | 1,526 |
-| POS (Ancora)| 16,678 | 13,123 | 1,709 | 1,846 |
-| STS         | 3,073 | 2,073 | 500 | 500 |
-| TC (TeCla) |  137,775 | 110,203 | 13,786 |  13,786|
-| QA (ViquiQuAD) | 14,239  | 11,255  | 1,492  | 1,429 |
-_The fine-tuning on downstream tasks have been performed with the HuggingFace [**Transformers**](https://github.com/huggingface/transformers) library_
-## Results
-Below the evaluation results on the CLUB tasks compared with the multilingual mBERT, XLM-RoBERTa models and
-the Catalan WikiBERT-ca model
-| Task        | NER (F1)      | POS (F1)   | STS (Pearson)   | TC (accuracy) | QA (ViquiQuAD) (F1/EM)  | QA (XQuAD) (F1/EM) |
-| ------------|:-------------:| -----:|:------|:-------|:------|:----|
-| BERTa       | **88.13** | **98.97** | **79.73** | **74.16** | **86.97/72.29** | **68.89/48.87** |
-| mBERT       | 86.38 | 98.82 | 76.34 | 70.56 | 86.97/72.22 | 67.15/46.51 |
-| XLM-RoBERTa | 87.66 | 98.89 | 75.40 | 71.68 | 85.50/70.47 | 67.10/46.42 |
-| WikiBERT-ca | 77.66 | 97.60 | 77.18 | 73.22 | 85.45/70.75 | 65.21/36.60 |
-## Intended uses & limitations
-The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
-However, the is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition.
----
-## Using BERTa
-## Load model and tokenizer
-``` python
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-tokenizer = AutoTokenizer.from_pretrained("bsc/roberta-base-ca-cased")
-model = AutoModelForMaskedLM.from_pretrained("bsc/roberta-base-ca-cased")
-```
-## Fill Mask task
-Below, an example of how to use the masked language modelling task with a pipeline.
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bsc/roberta-base-ca-cased')
->>> unmasker("Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
-             "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
-             "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
-             "i pel nord-oest per la serralada de Collserola "
-             "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
-             "la línia de costa encaixant la ciutat en un perímetre molt definit.")
-[
-  {
-    "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
-                "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
-                "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
-                "i pel nord-oest per la serralada de Collserola "
-                "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
-                "la línia de costa encaixant la ciutat en un perímetre molt definit.",
-    "score": 0.4177263379096985,
-    "token": 734,
-    "token_str": " Barcelona"
-  },
-  {
-    "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
-                "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
-                "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
-                "i pel nord-oest per la serralada de Collserola "
-                "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
-                "la línia de costa encaixant la ciutat en un perímetre molt definit.",
-    "score": 0.10696165263652802,
-    "token": 3849,
-    "token_str": " Badalona"
-  },
-  {
-    "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
-                "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
-                "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
-                "i pel nord-oest per la serralada de Collserola "
-                "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
-                "la línia de costa encaixant la ciutat en un perímetre molt definit.",
-    "score": 0.08135009557008743,
-    "token": 19349,
-    "token_str": " Collserola"
-  },
-  {
-   "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
-                "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
-                "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
-                "i pel nord-oest per la serralada de Collserola "
-                "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
-                "la línia de costa encaixant la ciutat en un perímetre molt definit.",
-    "score": 0.07330769300460815,
-    "token": 4974,
-    "token_str": " Terrassa"
-  },
-  {
-    "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada "
-                "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, "
-                "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa,"
-                "i pel nord-oest per la serralada de Collserola "
-                "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela "
-                "la línia de costa encaixant la ciutat en un perímetre molt definit.",
-    "score": 0.03317456692457199,
-    "token": 14333,
-    "token_str": " Gavà"
-  }
-]
-```

 ---
 # BERTa: RoBERTa-based Catalan language model
+<font size="+2">
+<strong>
+<span style="color:red">
+WARNING
+</span>
+</strong>
+</font>
+This repository is now superseded by [BSC-TeMU/roberta-base-ca-cased](https://huggingface.co/BSC-TeMU/roberta-base-ca-cased). Future updates will be released in the new repository, so it is highly recommended to load the model using the new path:
 ```python
 from transformers import AutoModel
 model = AutoModel.from_pretrained("BSC-TeMU/roberta-base-ca-cased")
 ```
+From now on, all models and datasets from the BSC's Text Mining Unit will be published on the [official organization account](https://huggingface.co/BSC-TeMU).