
This checkpoint has been trained for the POS task using the CoNLL 2002-es dataset.

This checkpoint was created from **Bertin Gaussian 512**, which is a **RoBERTa-base** model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin-base-gaussian-exp-512seqlen) and at deeper detail on [the main project card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish). 

The training dataset for the base model is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small values (short, repetitive texts).

This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.


## Team members

- Eduardo González ([edugp](https://huggingface.co/edugp))
- Javier de la Rosa ([versae](https://huggingface.co/versae))
- Manu Romero ([mrm8488](https://huggingface.co/))
- María Grandury ([mariagrandury](https://huggingface.co/))
- Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps))
- Paulo Villegas ([paulo](https://huggingface.co/paulo))
{"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "ner"]}
transformers pytorch safetensors roberta token-classification spanish ner es license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #spanish #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 


This checkpoint has been trained for the POS task using the CoNLL 2002-es dataset.

This checkpoint was created from Bertin Gaussian 512, which is a RoBERTa-base model trained from scratch in Spanish. Information on this base model may be found at its own card and at deeper detail on the main project card. 

The training dataset for the base model is mc4 subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small values (short, repetitive texts).

This is part of the
Flax/Jax Community Week, organised by HuggingFace and TPU usage sponsored by Google.


## Team members

- Eduardo González (edugp)
- Javier de la Rosa (versae)
- Manu Romero (mrm8488)
- María Grandury (mariagrandury)
- Pablo González de Prado (Pablogps)
- Paulo Villegas (paulo)
## Team members

- Eduardo González (edugp)
- Javier de la Rosa (versae)
- Manu Romero (mrm8488)
- María Grandury (mariagrandury)
- Pablo González de Prado (Pablogps)
- Paulo Villegas (paulo)
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #spanish #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Team members

- Eduardo González (edugp)
- Javier de la Rosa (versae)
- Manu Romero (mrm8488)
- María Grandury (mariagrandury)
- Pablo González de Prado (Pablogps)
- Paulo Villegas (paulo)
passage: TAGS
#transformers #pytorch #safetensors #roberta #token-classification #spanish #ner #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
## Team members

- Eduardo González (edugp)
- Javier de la Rosa (versae)
- Manu Romero (mrm8488)
- María Grandury (mariagrandury)
- Pablo González de Prado (Pablogps)
- Paulo Villegas (paulo)