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
twitter-roberta-base-sentiment-latest
by Cardiff NLP cardiffnlp/twitter-roberta-base-sentiment-latest
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark.
Parameters—
Context514
Weights999.9 MB
Licensecc-by-4.0
AccessOpen weights
Monthly Downloads3.1M
SAVRN's Notes on twitter-roberta-base-sentiment-latest
Three labels, English only, roughly 124 million tweets from January 2018 through December 2021: that is the whole job description. Negative, neutral or positive, one call per short text, from a 12-layer RoBERTa base whose 514-token window matches the posts it learned from. The page carries no sizing table, so size it from the files: just under 1 GB of weights stored at float32, small enough to fold into an inference box you already run.
CC BY 4.0 permits commercial use on two conditions: credit the creator, Cardiff NLP, and indicate any changes, which bites the moment you retrain it. The tweets end in December 2021, so run a sample of your current text through it before trusting the labels. It was tuned on TweetEval, so text that does not read like a tweet sits outside its brief. The repository was last updated in August 2025.
Model Card
By Cardiff NLP, published under cc-by-4.0, revision 3216a57f2a0d.
Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English.
- Reference Paper: TimeLMs paper.
- Git Repo: TimeLMs official repository.
Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive
This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.
Example Pipeline
from transformers import pipeline
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("Covid cases are increasing fast!")
[{'label': 'Negative', 'score': 0.7236}]
Full classification example
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
- cardiffnlp/twitter-roberta-base-sentiment-latest
- Publisher
- Cardiff NLP
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 3216a57f2a0d9c45a2e6c20157c20c49fb4bf9c7
- First published
- 2022-03-15
- Last updated
- 2025-08-04
Files and Weights
8 files, 1.0 GB in total. The weights are 2 files totalling 999.9 MB in bin, h5.
Weights2 files · 999.9 MB
Configuration2 files · 1.2 KB
Tokenizer2 files · 1.4 MB
Documentation1 file · 4.3 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 501.0 MB | 4d24a3e32a88 |
| tf_model.h5 | Weights | 498.8 MB | 682358ffb386 |
| config.json | Configuration | 929 B | — |
| special_tokens_map.json | Configuration | 239 B | — |
| README.md | Documentation | 4.3 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| merges.txt | Tokenizer | 456.3 KB | — |
| vocab.json | Tokenizer | 898.8 KB | — |
License and Download
- License
- cc-by-4.0
- Access
- Open weights, no gate
- Download size
- 999.9 MB
Download from Cardiff NLP
Released by Cardiff NLP through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2202.03829
- Trained on (disclosed) tweet_eval
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 999.9 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About twitter-roberta-base-sentiment-latest
Can I use twitter-roberta-base-sentiment-latest commercially?
Yes. twitter-roberta-base-sentiment-latest is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.
What is twitter-roberta-base-sentiment-latest's context length?
514 tokens, from the maximum position embeddings in its published configuration.
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