Instructions to use senfu/bert-base-uncased-finetuned-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use senfu/bert-base-uncased-finetuned-qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="senfu/bert-base-uncased-finetuned-qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("senfu/bert-base-uncased-finetuned-qnli") model = AutoModelForSequenceClassification.from_pretrained("senfu/bert-base-uncased-finetuned-qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "bert-base-uncased", | |
| "architectures": [ | |
| "ToPBertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "do_layer_distill": false, | |
| "finetuning_task": "qnli", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "entailment", | |
| "1": "not_entailment" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "entailment": 0, | |
| "not_entailment": 1 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_attentions": true, | |
| "output_hidden_states": true, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "pruned_heads": { | |
| "0": [], | |
| "1": [], | |
| "2": [], | |
| "3": [], | |
| "4": [], | |
| "5": [], | |
| "6": [], | |
| "7": [], | |
| "8": [], | |
| "9": [], | |
| "10": [], | |
| "11": [] | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.15.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |