transformers pytorch roberta feature-extraction token-classification en dataset:tner/bc5cdr dataset:tner/bionlp2004 dataset:tner/btc dataset:tner/conll2003 dataset:tner/fin dataset:tner/mit_movie_trivia dataset:tner/mit_restaurant dataset:tner/multinerd dataset:tner/ontonotes5 dataset:tner/tweebank_ner dataset:tner/tweetner7 dataset:tner/wikineural dataset:tner/wnut2017 endpoints_compatible region:us
---
datasets:
- tner/bc5cdr
- tner/bionlp2004
- tner/btc
- tner/conll2003
- tner/fin
- tner/mit_movie_trivia
- tner/mit_restaurant
- tner/multinerd
- tner/ontonotes5
- tner/tweebank_ner
- tner/tweetner7
- tner/wikineural
- tner/wnut2017
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: token-classification
---
# RoBERTa Span Detection 
This model is a fine-tuned model of [roberta-large](https://huggingface.co/roberta-large) after being trained on a **mixture of NER datasets**.

Basically, this model can detect NER spans (with <u>no differenciation on classes</u>). Labels use the IBO format and are: 
- 'B-TAG': beginning token of span
- 'I-TAG': inside token of span
- 'O': token not a span

# Usage
This model has been trained in an efficient way and thus cannot be load directly from HuggingFace's hub. To use that model, please follow instructions on this [repo](https://github.com/AntoineBlanot/efficient-llm).

# Data used for training
- tner/bc5cdr
- tner/bionlp2004
- tner/btc
- tner/conll2003
- tner/fin
- tner/mit_movie_trivia
- tner/mit_restaurant
- tner/multinerd
- tner/ontonotes5
- tner/tweebank_ner
- tner/tweetner7
- tner/wikineural
- tner/wnut2017

# Evaluation results

| Data | Accuracy |
|:---:|:---------:|
| validation | 0.972 |