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
toxic-comment-model
by Martin Pan martin-ha/toxic-comment-model
This model is a fine-tuned version of the DistilBERT model to classify toxic comments. You can use the model with the following code. This model is intended to use for classify toxic online classifications.
Parameters—
Context512
Weights267.9 MB
License—
AccessOpen weights
Monthly Downloads261k
Model Card
This model is a fine-tuned version of the DistilBERT model to classify toxic comments. You can use the model with the following code. This model is intended to use for classify toxic online classifications. However, one limitation of the model is that it performs poorly for some comments that mention a specific identity subgroup, like Muslim. The following table shows a evaluation score for different identity group. You can learn the specific meaning of this metrics here. But basically, those metrics shows how well a model performs for a specific group. The larger the number, the better. The table above shows that the model performs poorly for the muslim and jewish group. In fact, you pass…
Excerpt from the card by Martin Pan.
Configuration
- Architecture
- DistilBertForSequenceClassification
- Context length (tokens)
- 512
- Vocabulary size
- 30,522
- Stored precision
- float32
- Model type
- distilbert
Identity and Version
- Repository
- martin-ha/toxic-comment-model
- Publisher
- Martin Pan
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 9842c08b35a4687e7b211187d676986c8c96256d
- First published
- 2022-03-02
- Last updated
- 2022-05-06
Files and Weights
8 files, 268.6 MB in total. The weights are 1 file totalling 267.9 MB in bin.
Weights1 file · 267.9 MB
Configuration2 files · 816 B
Tokenizer3 files · 698.0 KB
Documentation1 file · 3.2 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 267.9 MB | 569aed60978b |
| config.json | Configuration | 704 B | — |
| special_tokens_map.json | Configuration | 112 B | — |
| README.md | Documentation | 3.2 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| tokenizer.json | Tokenizer | 466.1 KB | — |
| tokenizer_config.json | Tokenizer | 403 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 267.9 MB
Download from Martin Pan
Released by Martin Pan through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 267.9 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About toxic-comment-model
What is toxic-comment-model's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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