Text Classification
Transformers
PyTorch
English
bert
finance
sentiment analysis
regression
sentence bert
text-embeddings-inference
Instructions to use LHF/FinEAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LHF/FinEAS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LHF/FinEAS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LHF/FinEAS") model = AutoModelForSequenceClassification.from_pretrained("LHF/FinEAS") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": "old_models/bert-base-nli-stsb-mean-tokens/0_BERT/special_tokens_map.json", "name_or_path": "sentence-transformers/bert-base-nli-stsb-mean-tokens", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |