Instructions to use NTA1802/Pretrained-GPT2-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTA1802/Pretrained-GPT2-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTA1802/Pretrained-GPT2-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTA1802/Pretrained-GPT2-Classification") model = AutoModelForSequenceClassification.from_pretrained("NTA1802/Pretrained-GPT2-Classification") - Notebooks
- Google Colab
- Kaggle
File size: 960 Bytes
0754f03 | 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 42 43 | {
"activation_function": "gelu_new",
"architectures": [
"GPT2ForSequenceClassification"
],
"attn_pdrop": 0.1,
"bos_token_id": 0,
"embd_pdrop": 0.1,
"eos_token_id": 2,
"id2label": {
"0": "Negative",
"1": "Positive"
},
"initializer_range": 0.02,
"label2id": {
"Negative": 0,
"Positive": 1
},
"layer_norm_epsilon": 1e-05,
"model_type": "gpt2",
"n_ctx": 256,
"n_embd": 256,
"n_head": 4,
"n_inner": null,
"n_layer": 6,
"n_positions": 256,
"pad_token_id": 1,
"problem_type": "single_label_classification",
"reorder_and_upcast_attn": false,
"resid_pdrop": 0.1,
"scale_attn_by_inverse_layer_idx": false,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"torch_dtype": "float32",
"transformers_version": "4.53.1",
"use_cache": true,
"vocab_size": 20000
}
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