README.md

---
language:
- en
- es
- ca
licence: apache-2.0
tags:
- spanish
- catalan
- falcon-7b
datasets:
- BSC-LT/open_data_26B_tokens_balanced_es_ca
metrics:
- ppl
model-index:
- name: falcon_7b_balanced_tokenizer_fp16_CPT_open_data_26B_tokens_balanced_es_ca
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: BSC-LT/open_data_26B_tokens_balanced_es_ca
      type: Causal Language Modeling
      config: default
      split: validation
      args: default
    metrics:
    - name: Perplexity
      type: ppl
      value: 8.59
widget:
- text: |-
    Respòn a la pregunta següent.
    Pregunta: "Qui viu a França?"
    Resposta: "A França viuen els francesos."
    ----
    Respòn a la pregunta següent.
    Pregunta: "Quina és la capital de Suècia?"
    Resposta: "La capital de Suècia és Estocolm."
    ----
    Respòn a la pregunta següent.
    Pregunta: "Quina beguda es consumeix als matins per despertar-se?"
    Resposta: "La majoria de gent consumeix cafè per despertar-se."
    ----
    Respòn a la pregunta següent.
    Pregunta: "Qui és Leo Messi?"
    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
license: apache-2.0
pipeline_tag: text-generation
---

# falcon_7b_balanced_tokenizer_fp16_CPT_open_data_26B_tokens_balanced_es_ca

## 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)
- [Language adaptation](#language-adaptation)
- [Training](#training)
  - [Training data](#training-data)
  - [Training procedure](#training-procedure)
- [Additional information](#additional-information)
  - [Author](#author)
  - [Contact information](#contact-information)
  - [Copyright](#copyright)
  - [Licensing information](#licensing-information)
  - [Funding](#funding)
  - [Disclaimer](#disclaimer)

</details>

## Model description

The **Cǒndor-7B** is a transformer-based causal language model for Catalan, Spanish, and English. It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token trilingual corpus collected from publicly available corpora and crawlers.


## Intended uses and limitations

The **Cǒndor-7B** model is ready-to-use only for causal language modeling to perform text-generation tasks. However, it is intended to be fine-tuned on a generative downstream task.

## How to use

Here is how to use this model:

```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

input_text = "Maria y Miguel no tienen ningún "
model = "BSC-LT/condor-7b"
tokenizer = AutoTokenizer.from_pretrained(model)

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
generation = pipeline(
    input_text,
    max_length=200,
    do_sample=True,
    top_k=10,
    eos_token_id=tokenizer.eos_token_id,
)

print(f"Result: {generation['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. 


## Language adaptation

We adapted the original Falcon-7B model to Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer. The adaptation procedure is explained in this [blog](https://medium.com/@mpamies247/ee1ebc70bc79).

### New vocabulary
We trained a new BPE Tokenizer for the Catalan and Spanish languages (equal representation). We shuffled a small amount of English in the mixture (since English is in the model training data).
The resulting data has the following language distribution:

|Language|%|
|---|---|
|En|16.84%|
|Es|41.38%|
|Ca|41.79%|

This reduced drastically the number of tokens required to tokenize a text in the target language while the English tokenization shows a small increase.

### Embedding Layer Initialization
In order to fully take advantage of the English Pre-Training of the original Falcon model, we decided to re-use the embedding weights of the original model for those tokens shared between the two Tokenizers (the new and the old one). The rest of the embedding weights are initialized as the mean value of the weights of the original Tokenizer.


## Training

### Training data

The training corpus consists 26B tokens of several corpora gathered from web crawlings and public corpora.

| Dataset             | Language | Tokens (pre-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 |
| RacoCatalá Noticias | ca       |             17.16M |  2.142216727 |
| RacoCatalá Forums   | ca       |            333.73M |  2.142216727 |
| CaWaC               | ca       |             57.79M |  2.142216727 |
| Wikipedia           | ca       |            228.01M |  3.570361212 |
| Vilaweb             | ca       |             50.34M |  2.142216727 |

The dataset has the following language distribution:

|Language|%|
|---|---|
|En|16.84%|
|Es|41.38%|
|Ca|41.79%|

## Training procedure

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 50,257 tokens. Once the model has been successfully initialized, we continued its pre-training in the three target languages: Catalan, Spanish, and English. We kept a small amount of English in order to avoid catastrophic forgetting. The training lasted a total of 96 hours with 8 NVIDIA H100 GPUs of 80GB of RAM. 


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3

## Additional information

### Author
Language Technologies Unir at the Barcelona Supercomputing Center ([email protected])

### Contact information
For further information, send an email to [email protected]

### Copyright
Copyright (c) 2023 Langtech Unit at Barcelona Supercomputing Center 

### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Funding
This work was funded by 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). This work was also partially funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the Plan-TL.

### Disclaimer

<details>
<summary>Click to expand</summary>

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their 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 models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

</details>