Transformers
PyTorch
Safetensors
English
t5
text2text-generation
finbert
financial-sentiment-analysis
sentiment-analysis
text-generation-inference
Instructions to use amphora/FinABSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amphora/FinABSA with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("amphora/FinABSA") model = AutoModelForSeq2SeqLM.from_pretrained("amphora/FinABSA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c807fe1948b389a6fd06d0bdf1c84f731026d90211893b704f0e4baa42d3856f
- Size of remote file:
- 2.95 GB
- SHA256:
- f6574a17f6419a3e853e482d4ed1108cc3ceb347dc65fae38a3094e10cdedac9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.