FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally…
Open-weight model · Text classification
roberta_toxicity_classifier
by S NLP s-nlp/roberta_toxicity_classifier
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples.
Parameters—
Context514
Weights501.0 MB
Licenseopenrail++
AccessOpen weights
Monthly Downloads261.6k
Model Card
By S NLP, published under openrail++, revision 048c25bb1e19.
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa model (RoBERTa: A Robustly Optimized BERT Pretraining Approach) on it. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the AUC-ROC of 0.98 and F1-score of 0.76. To acknowledge our work, please, use the corresponding citation: This model is licensed under the OpenRAIL++ License, which supports the development of various technologies—both industrial and academic—that serve…
Read S NLP's full model card
Toxicity Classification Model
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa model (RoBERTa: A Robustly Optimized BERT Pretraining Approach) on it. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the AUC-ROC of 0.98 and F1-score of 0.76.
How to use
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
tokenizer = RobertaTokenizer.from_pretrained('s-nlp/roberta_toxicity_classifier')
model = RobertaForSequenceClassification.from_pretrained('s-nlp/roberta_toxicity_classifier')
batch = tokenizer.encode("You are amazing!", return_tensors="pt")
output = model(batch)
# idx 0 for neutral, idx 1 for toxic
Citation
To acknowledge our work, please, use the corresponding citation:
@inproceedings{logacheva-etal-2022-paradetox,
title = "{P}ara{D}etox: Detoxification with Parallel Data",
author = "Logacheva, Varvara and
Dementieva, Daryna and
Ustyantsev, Sergey and
Moskovskiy, Daniil and
Dale, David and
Krotova, Irina and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.469",
pages = "6804--6818",
abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.",
}
Licensing Information
This model is licensed under the OpenRAIL++ License, which supports the development of various technologies—both industrial and academic—that serve the public good.
Configuration
- Architecture
- RobertaForSequenceClassification
- Context length (tokens)
- 514
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 50,265
- Stored precision
- float32
- Model type
- roberta
Identity and Version
- Repository
- s-nlp/roberta_toxicity_classifier
- Publisher
- S NLP
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 048c25bb1e199b98802784f96325f4840f22145d
- First published
- 2022-03-02
- Last updated
- 2024-11-08
Files and Weights
8 files, 502.3 MB in total. The weights are 1 file totalling 501.0 MB in bin.
Weights1 file · 501.0 MB
Configuration2 files · 1.0 KB
Tokenizer3 files · 1.3 MB
Documentation1 file · 3.4 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 501.0 MB | 896f52af1617 |
| config.json | Configuration | 794 B | — |
| special_tokens_map.json | Configuration | 239 B | — |
| README.md | Documentation | 3.4 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| merges.txt | Tokenizer | 456.4 KB | — |
| tokenizer_config.json | Tokenizer | 25 B | — |
| vocab.json | Tokenizer | 798.3 KB | — |
License and Download
- License
- openrail++
- Access
- Open weights, no gate
- Download size
- 501.0 MB
Download from S NLP
Released by S NLP through its official repository on Hugging Face.
Built From
- Derived from FacebookAI/roberta-large
- Described by arXiv:1907.11692
- Trained on (disclosed) google/jigsaw_toxicity_pred
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 501.0 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About roberta_toxicity_classifier
Can I use roberta_toxicity_classifier commercially?
Yes, with conditions. roberta_toxicity_classifier is released under Open RAIL++ License. Open RAIL++ permits use, including commercial use, subject to the use-based restrictions listed in the license, which must be passed on to downstream users.
What is roberta_toxicity_classifier's context length?
514 tokens, from the maximum position embeddings in its published configuration.
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