Upload README.md

README.md

@@ -1,3 +1,247 @@
 ---
-license: apache-2.0
+language:
+- en
+- es
+- ca
+licence:
+- apache-2.0
+tags:
+- cabloom
+- bloom
+- spanish
+- catalan
+pipeline_tag: text-generation
+widget:
+- text: |-
+    Respon a la pregunta següent.
+    Pregunta: "Quina és la capital de Suècia?"
+    Resposta: "La capital de Suècia és Estocolm."
+    ----
+    Respon a la pregunta següent.
+    Pregunta: "Quina beguda es consumeix als matins per despertar-se?"
+    Resposta: "La majoria de gent consumeix cafè per despertar-se."
+    ----
+    Respon a la pregunta següent.
+    Pregunta: "Explica com funciona un motor de combustió"
+    Resposta:
+  example_title: Pregunta-Resposta
+- text: |-
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Me llamo Wolfgang y vivo en Berlin"
+    Entidades: Wolfgang:PER, Berlin:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center"
+    Entidades: parc güell:LOC, barcelona supercomputing center:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Maria y Miguel no tienen ningún problema contigo"
+    Entidades: Maria:PER, Miguel:PER
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Damián se cortó el pelo"
+    Entidades: Damián:PER
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Lo mejor de Barcelona és el bar de mi amigo Pablo"
+    Entidades: Pablo:PER, Barcelona:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Carlos comparte piso con Marc"
+    Entidades:
+  example_title: Entidades-Nombradas
 ---
+
+# CaBLOOM-760M
+
+## Table of Contents
+<details>
+<summary>Click to expand</summary>
+
+- [Model description](#model-description)
+- [Intended uses and limitations](#intended-uses-and-limitations)
+- [How to use](#how-to-use)
+- [Limitations and bias](#limitations-and-bias)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Additional information](#additional-information)
+
+</details>
+
+## Model description
+
+**CaBLOOM-760M** is a 760M-parameter transformer-based causal language model for Catalan, Spanish, and English. 
+It is the result of a language adaptation technique performed on [BLOOM-1.1B](https://huggingface.co/bigscience/bloom-1b1), 
+which involves modifying the model's vocabulary and embedding layer, and continuously pre-training the model with 26B tokens in our target languages.
+
+This model has been developed as part of a scientific research submitted to [LREC-COLING 2024](https://lrec-coling-2024.org/), and is currently undergoing a peer review process.
+
+## Intended uses and limitations
+
+The **CaBLOOM-760M** model is ready-to-use only for causal language modeling. 
+It can perform text-generation tasks and be fine-tuned for specific scenarios.
+
+## How to use
+```python
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+input_text = "Sovint em trobo pensant en tot allò que"
+
+model_id  = "BSC-LT/CaBLOOM-760M"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+generator = pipeline(
+    "text-generation",
+    model=model_id,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+generation = generator(
+    input_text,
+    do_sample=True,
+    top_k=10,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+print(f"Result: {generation[0]['generated_text']}")
+```
+
+## Limitations and bias
+At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. 
+However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques 
+on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. 
+
+
+## Training
+
+### Language adaptation and training
+
+The language adaptation technique used to create CaBLOOM-1.3B requires the vocabulary of the source model 
+to be adapted before continuing its pre-training with data in the target languages. Specifically, we proceeded as follows:
+1) We trained our own BPE tokenizer for Catalan, Spanish, and English, and replaced the original BLOOM tokenizer and vocabulary with it. This procedure implied a downsizing of the original BLOOM's embedding layer and, therefore, a model compression from 1.7B parameters to 1.3B.
+2) The embeddings corresponding to tokens that are present in both the original and the target vocabulary (matching tokens) were used for initialization.
+3) The embeddings from tokens not present in BLOOM's original vocabulary were initialized as the average of all embeddings.
+4) The model was initialized with the weights from BOOM-1.7B, and with our adapted tokenizer (step 1) and embeddings (steps 2-3).
+5) The model was then trained on a corpus that contains a mixture of Catalan, Spanish and English data.
+
+### Training data
+
+The training corpus is the same that was used to train [Ǎguila-7B](https://huggingface.co/projecte-aina/aguila-7b).
+It consists of 26B tokens of several corpora gathered from web crawlings and public domain data.
+
+| Dataset             | Language | Words (per-epoch) | Epochs       |
+|---------------------|----------|--------------------|--------------|
+| Wikipedia           | en       |           2169.97M |  1.428144485 |
+| C4_es               | es       |          53709.80M | 0.1049686196 |
+| Biomedical          | es       |            455.03M | 0.7140722425 |
+| Legal               | es       |            995.70M | 0.7140722425 |
+| Wikipedia           | es       |            693.60M |  1.428144485 |
+| Gutenberg           | es       |             53.18M | 0.7140722425 |
+| C4_ca               | ca       |           2826.00M |  2.142216727 |
+| Biomedical          | ca       |             11.80M |  1.428144485 |
+| RacoCatalà Noticias | ca       |             17.16M |  2.142216727 |
+| RacoCatalà Forums   | ca       |            333.73M |  2.142216727 |
+| CaWaC               | ca       |             57.79M |  2.142216727 |
+| Wikipedia           | ca       |            228.01M |  3.570361212 |
+| Vilaweb             | ca       |             50.34M |  2.142216727 |
+
+### Languages
+
+The training data has the same amount of Catalan and Spanish texts, and a smaller amount of English data. 
+The table below shows the final language distribution:
+
+|Language|Percentage|
+|--------|----------|
+|   English (EN)   |  16.84%  |
+|   Spanish (ES)   |  41.38%  |
+|   Catalan (CA)   |  41.79%  |
+
+### Training hyperparameters
+- seed: 1
+- distributed_type: [WSE-2](https://www.cerebras.net/product-chip/)
+- num_devices: 1
+- train_batch_size: 60
+- eval_batch_size:  60
+- optimizer: AdamW
+- betas: (0.9,0.95)
+- epsilon: 1e-08
+- weight_decay_rate: 0.1
+- learning_rate:
+  - scheduler: "Linear"
+    initial_learning_rate: 0.0
+    end_learning_rate: 4.1e-5
+    steps: 3050
+  - scheduler: "CosineDecay"
+    initial_learning_rate: 4.1e-5
+    end_learning_rate: 3.4e-6
+    steps: 209133
+  - scheduler: "Constant"
+    learning_rate: 2.2e-6
+- num_epochs: 1.0
+
+### Framework versions
+The training was conducted in a Cerebras' [CS-2 system](https://www.cerebras.net/product-system/) 
+using the [cs-1.9.1](https://github.com/Cerebras/modelzoo/releases/tag/Release_1.9.1) release of their software.
+
+
+## Evaluation
+CaBLOOM-760M has been evaluated on 5-shot, using EleutherAI's Evaluation Harness implementation, on several datasets in Catalan, Spanish and English, with particular emphasis on Catalan datasets. 
+
+The tasks were chosen to cover several evaluation areas in order to provide a comprehensive overview of the model's capabilities. The baselines used to compare our results are multilingual and English open-source 1.3B models: mGPT-1.3B, GPT-Neo-1.3B, Pythia-1.4B, OPT-1.3B, Falcon-rw-1.3B, and Cerebras-GPT-1.3B.
+
+Our implementation of EleutherAI's *LM Evaluation Harness* can be found [here](https://github.com/langtech-bsc/lm-evaluation-harness/tree/cabloom-eval).
+
+The following is a list of evaluation areas and their respective datasets:
+- Reading Comprehension: [Belebele](https://huggingface.co/datasets/facebook/belebele)
+- Question Answering: [XQuAD](https://huggingface.co/datasets/xquad), [CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa), [CoQCat](https://huggingface.co/datasets/projecte-aina/CoQCat)
+- Natural Language Inference: [XNLI](https://huggingface.co/datasets/xnli) and its translation to Catalan ([XNLI-ca](https://huggingface.co/datasets/projecte-aina/xnli-ca)), [TE-ca](https://huggingface.co/datasets/projecte-aina/teca)
+- Paraphrase Identification: [PAWS-X](https://huggingface.co/datasets/paws-x) and its translation to Catalan ([PAWS-ca](https://huggingface.co/datasets/projecte-aina/PAWS-ca)), [Parafraseja](https://huggingface.co/datasets/projecte-aina/Parafraseja)
+- Commonsense Reasoning: [COPA](https://people.ict.usc.edu/~gordon/copa.html) and its translation to Catalan ([COPA-ca](https://huggingface.co/datasets/projecte-aina/COPA-ca))
+- Translation: [FLoRes](https://huggingface.co/datasets/flores)
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/635ba692dc371b8f91005172/o595pF7dw-iTuR1_x4MVy.png)
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/635ba692dc371b8f91005172/DhrkZG8Xqob7Ml4n6zQcY.png)
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/635ba692dc371b8f91005172/PxgzqXAelUoWY-23zXvPm.png){ width: 200px; }
+
+
+## Additional information
+
+### Author
+The Language Technologies Unit from Barcelona Supercomputing Center.
+
+### Contact
+For further information, please send an email to <langtech@bsc.es>.
+
+### Copyright
+Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
+
+### License
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Funding
+This work was funded by [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+### Disclaimer
+
+<details>
+<summary>Click to expand</summary>
+
+The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0. 
+
+Be aware that the model may have biases and/or any other undesirable distortions.
+
+When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) 
+or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, 
+in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
+
+In no event shall the owner and creator of the model (Barcelona Supercomputing Center) 
+be liable for any results arising from the use made by third parties.
+
+</details>