This model was fine-tuned on the AG News dataset (fancyzhx/agnews) for four-class news topic classification: The dataset was divided into 108,000 training examples, 12,000 validation examples, and 7,600 test examples. A random seed of 42 was used. This model is intended for English news topic classification into the four AG News categories: World, Sports, Business, and Sci/Tech. It was developed for educational purposes and experimentation with BERT adaptation methods. The model is trained on English news data and may not generalize well to other domains or languages. It only supports the four categories present in AG News. Performance on real-world data may differ from the reported…
finbert is an open-weight model for text classification from Prosus AI, released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0. It has 109M parameters and a 512-token context. At 16-bit it needs about 0.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 4.3M downloads a month.
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.
Parameters109M
Context512
Weights1.8 GB
Licensecc-by-nc-sa-4.0
AccessOpen weights
Monthly Downloads4.3M
Runs On
What it takes to serve finbert (109M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 13, 2026.
finbert on every accelerator the SAVRN Index prices, at every precision
SAVRN's Notes on finbert
Three labels, positive, negative or neutral, on a passage of at most 512 tokens: that is the whole job. The page carries no memory figure, and with 12 layers and a 768 hidden size the sizing conversation is throughput, how many 512-token windows per second you need scored. The download is 1.3 GB across 9 files covering pytorch, jax and tf copies, so one framework loads a fraction of that.
The part that stops a deployment: the license field on this page is empty, so there are no terms to read and a buyer has to get them from Prosus AI before any commercial use. Beyond that, respect the 512-token ceiling, since longer documents get chunked and scored in pieces, and know where the labels come from: continued BERT training on financial text and fine-tuning on Financial PhraseBank, described in arXiv:1908.10063.
Model Card
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. To get the scores for all three labels, pass topk=None to the classifier. This model was trained using the Financial PhraseBank dataset: The…
Excerpt from the card by Prosus AI, licensed cc-by-nc-sa-4.0.
Configuration
- Architecture
- BertForSequenceClassification
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 30,522
- Model type
- bert
Identity and Version
- Repository
- ProsusAI/finbert
- Publisher
- Prosus AI
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 109M parameters
- Languages
- en
- Revision
- 0bc2ac10dda6929bcb1f77ccc43c7ec5c424f031
- First published
- 2022-03-02
- Last updated
- 2026-10-12
Files and Weights
10 files, 1.8 GB in total. The weights are 4 files totalling 1.8 GB in bin, h5, msgpack, safetensors.
Weights4 files · 1.8 GB
Configuration2 files · 870 B
Tokenizer2 files · 231.8 KB
Documentation1 file · 4.6 KB
Repository1 file · 445 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| flax_model.msgpack | Weights | 437.9 MB | 04c231ff252c |
| model.safetensors | Weights | 438.0 MB | e5897858ff81 |
| pytorch_model.bin | Weights | 438.0 MB | e15a7b5738df |
| tf_model.h5 | Weights | 438.2 MB | 195e23248e2e |
| config.json | Configuration | 758 B | — |
| special_tokens_map.json | Configuration | 112 B | — |
| README.md | Documentation | 4.6 KB | — |
| .gitattributes | Repository | 445 B | — |
| tokenizer_config.json | Tokenizer | 252 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
License and Download
- License
- cc-by-nc-sa-4.0
- Access
- Open weights, no gate
- Download size
- 1.8 GB
Download from Prosus AI
Released by Prosus AI through its official repository on Hugging Face.
Built From
- Described by arXiv:1908.10063
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.8 GB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Built on This Model
- Quantized fromfinbert-GGUF
- Derived fromfinbert-GGUF
Questions About finbert
How much GPU memory does finbert need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run finbert on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use finbert commercially?
Not without separate permission. finbert is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0. CC BY-NC-SA 4.0 permits non-commercial sharing and adapting with credit, and requires adaptations to use the same license. Commercial use needs separate permission.
What is finbert's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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