Instructions to use steve-nguyen/movie_cls_gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use steve-nguyen/movie_cls_gpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="steve-nguyen/movie_cls_gpt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("steve-nguyen/movie_cls_gpt") model = AutoModelForSequenceClassification.from_pretrained("steve-nguyen/movie_cls_gpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_function": "gelu_new", | |
| "architectures": [ | |
| "GPT2ForSequenceClassification" | |
| ], | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 0, | |
| "dtype": "float32", | |
| "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": 512, | |
| "n_embd": 512, | |
| "n_head": 8, | |
| "n_inner": null, | |
| "n_layer": 6, | |
| "n_positions": 512, | |
| "pad_token_id": 2, | |
| "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, | |
| "transformers_version": "4.57.2", | |
| "use_cache": true, | |
| "vocab_size": 50257 | |
| } | |