Text Classification
Transformers
Safetensors
English
bert
finance
reddit
wallstreetbets
sentiment-analysis
NLP
BERT
FinBERT
FinTwitBERT
sentiment
financial-analysis
financial-sentiment-analysis
stocks
stock-market
crypto
cryptocurrency
text-embeddings-inference
Instructions to use StephanAkkerman/FinTwitBERT-wsb-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StephanAkkerman/FinTwitBERT-wsb-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StephanAkkerman/FinTwitBERT-wsb-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("StephanAkkerman/FinTwitBERT-wsb-sentiment") model = AutoModelForSequenceClassification.from_pretrained("StephanAkkerman/FinTwitBERT-wsb-sentiment") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "max_length": 512, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "never_split": null, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |