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: 986 Bytes
96668f9 5de80d0 1bd3b91 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 41 | {
"_name_or_path": "versae/gzip-bert",
"architectures": [
"RobertaForSequenceClassification"
],
"auto_map": {
"AutoTokenizer": "tokenization_gzip_bert.GzipBertTokenizer"
},
"tokenizer_class": "GzipBertTokenizer",
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "NEGATIVE",
"1": "POSITIVE"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"NEGATIVE": 0,
"POSITIVE": 1
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.30.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
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