Text Classification
PEFT
Safetensors
Transformers
lora
torchao
emotion-classification
int8
Eval Results (legacy)
Instructions to use Wb-az/peft-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Wb-az/peft-roberta-base with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("roberta-base") model = PeftModel.from_pretrained(base_model, "Wb-az/peft-roberta-base") - Transformers
How to use Wb-az/peft-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wb-az/peft-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Wb-az/peft-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Wb-az/peft-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 359 Bytes
-
https://huggingface.co/Wb-az/peft-roberta-base/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Wb-az/peft-roberta-base/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Wb-az/peft-roberta-base/resolve/main/tokenizer_config.json
359 Bytes
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "RobertaTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |