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
emotion-english-distilroberta-base
by Hartmann j-hartmann/emotion-english-distilroberta-base
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 2) disgust 3) fear 4) joy 5) neutral 6) sadness 7) surprise…
Parameters—
Context514
Weights657.2 MB
License—
AccessOpen weights
Monthly Downloads553.7k
Model Card
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 2) disgust 3) fear 4) joy 5) neutral 6) sadness 7) surprise The model is a fine-tuned checkpoint of DistilRoBERTa-base. For a 'non-distilled' emotion model, please refer to the model card of the RoBERTa-large version. a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab: b) Run emotion model on multiple examples and full datasets (e.g.,.csv files) on Google Colab: Please reach out to [email protected] if you have any…
Excerpt from the card by Hartmann.
Configuration
- Architecture
- RobertaForSequenceClassification
- Context length (tokens)
- 514
- Layers
- 6
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 50,265
- Model type
- roberta
Identity and Version
- Repository
- j-hartmann/emotion-english-distilroberta-base
- Publisher
- Hartmann
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 0e1cd914e3d46199ed785853e12b57304e04178b
- First published
- 2022-03-02
- Last updated
- 2023-01-02
Files and Weights
11 files, 659.8 MB in total. The weights are 3 files totalling 657.2 MB in bin, h5.
Weights3 files · 657.2 MB
Configuration2 files · 1.2 KB
Tokenizer4 files · 2.6 MB
Documentation1 file · 4.9 KB
Repository1 file · 690 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 328.5 MB | dde1eadd8174 |
| tf_model.h5 | Weights | 328.6 MB | c063ba3427b9 |
| training_args.bin | Weights | 2.4 KB | 4ed7a68d5439 |
| config.json | Configuration | 1.0 KB | — |
| special_tokens_map.json | Configuration | 239 B | — |
| README.md | Documentation | 4.9 KB | — |
| .gitattributes | Repository | 690 B | — |
| merges.txt | Tokenizer | 456.4 KB | — |
| tokenizer.json | Tokenizer | 1.4 MB | — |
| tokenizer_config.json | Tokenizer | 294 B | — |
| vocab.json | Tokenizer | 798.3 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 657.2 MB
Download from Hartmann
Released by Hartmann through its official repository on Hugging Face.
Built From
- Described by arXiv:2210.00434
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 657.2 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About emotion-english-distilroberta-base
What is emotion-english-distilroberta-base's context length?
514 tokens, from the maximum position embeddings in its published configuration.
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The output will be Higher scores indicate higher relevance. This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine. This tool shows the results of research conducted in the Computational Biology Branch, NCBI/NLM. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the…