Text Classification
Transformers
PyTorch
Safetensors
Polish
distilbert
sentiment-analysis
twitter
distiluse
Eval Results (legacy)
text-embeddings-inference
Instructions to use bardsai/twitter-sentiment-pl-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bardsai/twitter-sentiment-pl-fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/twitter-sentiment-pl-fast")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bardsai/twitter-sentiment-pl-fast") model = AutoModelForSequenceClassification.from_pretrained("bardsai/twitter-sentiment-pl-fast", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from bardsai/twitter-sentiment-pl-fast: direct link, hf CLI and curl.
- Browser
- Download file 391 Bytes
-
https://huggingface.co/bardsai/twitter-sentiment-pl-fast/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://bardsai/twitter-sentiment-pl-fast/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/bardsai/twitter-sentiment-pl-fast/resolve/main/tokenizer_config.json
391 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "max_len": 512, | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "DistilBertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |