Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mnavas/roberta-finetuned-WebClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mnavas/roberta-finetuned-WebClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mnavas/roberta-finetuned-WebClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mnavas/roberta-finetuned-WebClassification") model = AutoModelForSequenceClassification.from_pretrained("mnavas/roberta-finetuned-WebClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from mnavas/roberta-finetuned-WebClassification: direct link, hf CLI and curl.
- Browser
- Download file 398 Bytes
-
https://huggingface.co/mnavas/roberta-finetuned-WebClassification/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://mnavas/roberta-finetuned-WebClassification/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/mnavas/roberta-finetuned-WebClassification/resolve/main/tokenizer_config.json
398 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "xlm-roberta-base", "tokenizer_class": "XLMRobertaTokenizer"} |