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--- |
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language: |
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- en |
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- es |
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- ca |
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licence: |
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- apache-2.0 |
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tags: |
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- aguila |
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- falcon |
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- spanish |
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- catalan |
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metrics: |
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- ppl |
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model-index: |
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- name: aguila_7b |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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metrics: |
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- name: Perplexity |
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type: ppl |
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value: 8.59 |
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pipeline_tag: text-generation |
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widget: |
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- text: |- |
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Respon a la pregunta següent. |
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Pregunta: "Quina és la capital de Suècia?" |
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Resposta: "La capital de Suècia és Estocolm." |
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---- |
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Respon a la pregunta següent. |
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Pregunta: "Quina beguda es consumeix als matins per despertar-se?" |
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Resposta: "La majoria de gent consumeix cafè per despertar-se." |
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---- |
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Respon a la pregunta següent. |
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Pregunta: "Explica com funciona un motor de combustió" |
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Resposta: |
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example_title: Pregunta-Resposta |
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- text: |- |
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Extrae las entidades nombradas del siguiente texto: |
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Texto: "Me llamo Wolfgang y vivo en Berlin" |
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Entidades: Wolfgang:PER, Berlin:LOC |
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---- |
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Extrae las entidades nombradas del siguiente texto: |
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Texto: "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center" |
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Entidades: parc güell:LOC, barcelona supercomputing center:LOC |
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---- |
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Extrae las entidades nombradas del siguiente texto: |
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Texto: "Maria y Miguel no tienen ningún problema contigo" |
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Entidades: Maria:PER, Miguel:PER |
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---- |
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Extrae las entidades nombradas del siguiente texto: |
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Texto: "Damián se cortó el pelo" |
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Entidades: Damián:PER |
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---- |
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Extrae las entidades nombradas del siguiente texto: |
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Texto: "Lo mejor de Barcelona és el bar de mi amigo Pablo" |
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Entidades: Pablo:PER, Barcelona:LOC |
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---- |
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Extrae las entidades nombradas del siguiente texto: |
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Texto: "Carlos comparte piso con Marc" |
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Entidades: |
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example_title: Entidades-Nombradas |
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--- |
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# Ǎguila-7B |
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## Table of Contents |
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<details> |
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<summary>Click to expand</summary> |
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- [Model description](#model-description) |
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- [Intended uses and limitations](#intended-uses-and-limitations) |
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- [How to use](#how-to-use) |
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- [Limitations and bias](#limitations-and-bias) |
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- [Language adaptation](#language-adaptation) |
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- [Training](#training) |
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- [Training data](#training-data) |
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- [Training procedure](#training-procedure) |
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- [Additional information](#additional-information) |
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- [Author](#author) |
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- [Contact](#contact) |
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- [Copyright](#copyright) |
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- [License](#license) |
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- [Funding](#funding) |
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- [Disclaimer](#disclaimer) |
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</details> |
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## Model description |
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**Ǎguila-7B** is a transformer-based causal language model for Catalan, Spanish, and English. |
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It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token |
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trilingual corpus collected from publicly available corpora and crawlers. |
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More information available in the following post from Medium.com: Introducing Ǎguila, a new open-source LLM for Spanish and Catalan (https://medium.com/@mpamies247/introducing-a%CC%8Cguila-a-new-open-source-llm-for-spanish-and-catalan-ee1ebc70bc79) |
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## Intended uses and limitations |
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The **Ǎguila-7B** model is ready-to-use only for causal language modeling to perform text-generation tasks. |
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However, it is intended to be fine-tuned for downstream tasks. |
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## How to use |
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Here is how to use this model: |
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```python |
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import torch |
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM |
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input_text = "El mercat del barri és fantàstic, hi pots trobar" |
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model_id = "projecte-aina/aguila-7b" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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generator = pipeline( |
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"text-generation", |
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model=model_id, |
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tokenizer=tokenizer, |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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device_map="auto", |
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) |
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generation = generator( |
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input_text, |
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do_sample=True, |
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top_k=10, |
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eos_token_id=tokenizer.eos_token_id, |
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) |
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print(f"Result: {generation[0]['generated_text']}") |
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``` |
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## Limitations and bias |
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At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. |
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However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques |
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on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. |
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## Language adaptation |
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We adapted the original [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model to Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer. |
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The adaptation procedure is explained in [this blog post](https://medium.com/@mpamies247/ee1ebc70bc79). |
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## Training |
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### Training data |
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The training corpus consists of 26B tokens of several corpora gathered from web crawlings and public domain data. |
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| Dataset | Language | Words (per-epoch) | Epochs | |
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|---------------------|----------|--------------------|--------------| |
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| Wikipedia | en | 2169.97M | 1.428144485 | |
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| C4_es | es | 53709.80M | 0.1049686196 | |
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| Biomedical | es | 455.03M | 0.7140722425 | |
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| Legal | es | 995.70M | 0.7140722425 | |
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| Wikipedia | es | 693.60M | 1.428144485 | |
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| Gutenberg | es | 53.18M | 0.7140722425 | |
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| C4_ca | ca | 2826.00M | 2.142216727 | |
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| Biomedical | ca | 11.80M | 1.428144485 | |
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| RacoCatalà Noticias | ca | 17.16M | 2.142216727 | |
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| RacoCatalà Forums | ca | 333.73M | 2.142216727 | |
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| CaWaC | ca | 57.79M | 2.142216727 | |
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| Wikipedia | ca | 228.01M | 3.570361212 | |
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| Vilaweb | ca | 50.34M | 2.142216727 | |
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The dataset has the following language distribution: |
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|Language|Percentage| |
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|--------|----------| |
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| En | 16.84% | |
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| Es | 41.38% | |
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| Ca | 41.79% | |
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Note: A small amount of English data was kept to avoid catastrophic forgetting. |
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## Training procedure |
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The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) with a vocabulary size of 50,257 tokens. |
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After training a new tokenizer and adapting [falcon-7b](https://huggingface.co/tiiuae/falcon-7b)'s embedding layer, the model was |
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further pre-trained in three target languages: Catalan, Spanish and English. |
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The training lasted a total of 320 hours on 8 NVIDIA H100 GPUs with 80GB RAM. |
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### Training hyperparameters |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- total_train_batch_size: 8 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam |
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- betas: (0.9,0.999) |
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- epsilon: 1e-08 |
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- learning_rate: 5e-05 |
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- lr_scheduler_type: linear |
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- num_epochs: 1.0 |
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### Framework versions |
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- Pytorch 2.0.0 |
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- Transformers 4.30.2 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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## Additional information |
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### Author |
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The Language Technologies Unit from Barcelona Supercomputing Center. |
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### Contact |
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For further information, please send an email to <[email protected]>. |
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### Copyright |
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Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center. |
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### License |
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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### Funding |
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This work was funded by: |
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- The [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). |
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- The [Spanish State Secretariat for Digitalization and Artificial Intelligence](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the [Plan de Impulso de las Tecnologías del Lenguaje](https://plantl.mineco.gob.es/Paginas/index.aspx). |
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### Disclaimer |
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<details> |
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<summary>Click to expand</summary> |
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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. |
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Be aware that the model may have biases and/or any other undesirable distortions. |
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When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) |
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or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, |
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in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. |
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In no event shall the owner and creator of the model (Barcelona Supercomputing Center) |
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be liable for any results arising from the use made by third parties. |
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</details> |
