Instructions to use ji-xin/roberta_base-RTE-two_stage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ji-xin/roberta_base-RTE-two_stage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ji-xin/roberta_base-RTE-two_stage")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("ji-xin/roberta_base-RTE-two_stage", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RobertaForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "divide": "two_stage", | |
| "finetuning_task": "rte", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "num_labels": 2, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_past": true, | |
| "pruned_heads": {}, | |
| "torchscript": false, | |
| "type_vocab_size": 1, | |
| "use_bfloat16": false, | |
| "vocab_size": 50265 | |
| } | |