Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use rendchevi/roberta-large-tqacd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendchevi/roberta-large-tqacd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rendchevi/roberta-large-tqacd")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rendchevi/roberta-large-tqacd") model = AutoModelForSequenceClassification.from_pretrained("rendchevi/roberta-large-tqacd") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "All-or-Nothing", | |
| "1": "Catastrophizing", | |
| "2": "Emotional Reasoning", | |
| "3": "Fortune-telling", | |
| "4": "Labeling", | |
| "5": "Mental Filter", | |
| "6": "Mind Reading", | |
| "7": "No Distortion", | |
| "8": "Overgeneralization", | |
| "9": "Personalization", | |
| "10": "Should Statements" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "All-or-Nothing": 0, | |
| "Catastrophizing": 1, | |
| "Emotional Reasoning": 2, | |
| "Fortune-telling": 3, | |
| "Labeling": 4, | |
| "Mental Filter": 5, | |
| "Mind Reading": 6, | |
| "No Distortion": 7, | |
| "Overgeneralization": 8, | |
| "Personalization": 9, | |
| "Should Statements": 10 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "transformers_version": "4.57.1", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |