Instructions to use semaj83/ctmatch-clf-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use semaj83/ctmatch-clf-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="semaj83/ctmatch-clf-R")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("semaj83/ctmatch-clf-R") model = AutoModelForSequenceClassification.from_pretrained("semaj83/ctmatch-clf-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "not_relevant", | |
| "1": "partially_relevant", | |
| "2": "relevant" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "is_decoder": false, | |
| "label2id": { | |
| "not_relevant": 0, | |
| "partially_relevant": 1, | |
| "relevant": 2 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.12.1", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 28895 | |
| } | |