Instructions to use microsoft/DialogRPT-width with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/DialogRPT-width with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/DialogRPT-width")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/DialogRPT-width") model = AutoModelForSequenceClassification.from_pretrained("microsoft/DialogRPT-width", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from microsoft/DialogRPT-width: direct link, hf CLI and curl.
- Browser
- Download file 812 Bytes
-
https://huggingface.co/microsoft/DialogRPT-width/resolve/main/config.json
- Command line
-
hf download hf://microsoft/DialogRPT-width/config.json
-
curl -L -o config.json https://huggingface.co/microsoft/DialogRPT-width/resolve/main/config.json
812 Bytes
| { | |
| "activation_function": "gelu_new", | |
| "architectures": [ | |
| "GPT2ForSequenceClassification" | |
| ], | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 50256, | |
| "embd_pdrop": 0.1, | |
| "eos_token_id": 50256, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "gpt2", | |
| "n_ctx": 1024, | |
| "n_embd": 1024, | |
| "n_head": 16, | |
| "n_layer": 24, | |
| "n_positions": 1024, | |
| "n_special": 0, | |
| "predict_special_tokens": true, | |
| "resid_pdrop": 0.1, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "task_specific_params": { | |
| "text-generation": { | |
| "do_sample": true, | |
| "max_length": 50 | |
| } | |
| }, | |
| "vocab_size": 50257, | |
| "num_labels": 1, | |
| "pad_token_id": 50256 | |
| } | |