Instructions to use hf-tiny-model-private/tiny-random-CTRLForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-CTRLForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-CTRLForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-CTRLForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-CTRLForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "tiny_models/ctrl/CTRLForSequenceClassification", | |
| "architectures": [ | |
| "CTRLForSequenceClassification" | |
| ], | |
| "dff": 8192, | |
| "embd_pdrop": 0.1, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "ctrl", | |
| "n_embd": 32, | |
| "n_head": 4, | |
| "n_layer": 5, | |
| "n_positions": 512, | |
| "pad_token_id": 246533, | |
| "resid_pdrop": 0.1, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 246534 | |
| } | |