Text Classification
Transformers
PyTorch
Safetensors
Serbian
electra
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/BERTicSENTPOS4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTPOS4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTPOS4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTPOS4") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTPOS4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Tanor/BERTicSENTPOS4: direct link, hf CLI and curl.
- Browser
- Download file 389 Bytes
-
https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/7ff98da87f90eeb2e63e6f59db0aebafc710930c/tokenizer_config.json
- Command line
-
hf download hf://Tanor/BERTicSENTPOS4@7ff98da87f90eeb2e63e6f59db0aebafc710930c/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/7ff98da87f90eeb2e63e6f59db0aebafc710930c/tokenizer_config.json
389 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": false, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "ElectraTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |