# Bert Online Discussions (bert-web-discussions-en)

This model is a fine-tuned version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was introduced in [this paper](https://aclanthology.org/2022.acl-long.379/).

## Model description

The BERT base language model was fine-tuned on the [Webis-CMV-20 corpus](https://zenodo.org/record/3778298#.YxB-HC223RZ) and on the [args.me corpus](https://zenodo.org/record/3734893#.YxB-NC223RY).
The model was trained on a sample of 2,469,026 sentences in total.
{}
transformers pytorch bert feature-extraction endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us 

# Bert Online Discussions (bert-web-discussions-en)

This model is a fine-tuned version of the BERT base model. It was introduced in this paper.

## Model description

The BERT base language model was fine-tuned on the Webis-CMV-20 corpus and on the URL corpus.
The model was trained on a sample of 2,469,026 sentences in total.
# Bert Online Discussions (bert-web-discussions-en)

This model is a fine-tuned version of the BERT base model. It was introduced in this paper. ## Model description

The BERT base language model was fine-tuned on the Webis-CMV-20 corpus and on the URL corpus.
The model was trained on a sample of 2,469,026 sentences in total.
TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us 
 # Bert Online Discussions (bert-web-discussions-en)

This model is a fine-tuned version of the BERT base model. It was introduced in this paper. ## Model description

The BERT base language model was fine-tuned on the Webis-CMV-20 corpus and on the URL corpus.
The model was trained on a sample of 2,469,026 sentences in total.
passage: TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us 
# Bert Online Discussions (bert-web-discussions-en)

This model is a fine-tuned version of the BERT base model. It was introduced in this paper.## Model description

The BERT base language model was fine-tuned on the Webis-CMV-20 corpus and on the URL corpus.
The model was trained on a sample of 2,469,026 sentences in total.