Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
roberta
Generated from Trainer
custom_code
Eval Results (legacy)
Instructions to use versae/gzipbert_imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use versae/gzipbert_imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="versae/gzipbert_imdb", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("versae/gzipbert_imdb", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("versae/gzipbert_imdb", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 854 Bytes
96668f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"additional_special_tokens": [],
"clean_up_tokenization_spaces": true,
"eos_token": {
"__type": "AddedToken",
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"extra_ids": 0,
"mask_token": {
"__type": "AddedToken",
"content": "<mask>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"model_max_length": 1000000000000000019884624838656,
"pad_token": {
"__type": "AddedToken",
"content": "<pad>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"tokenizer_class": "GzipBertTokenizer",
"unk_token": {
"__type": "AddedToken",
"content": "<unk>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
}
}
|