Text Classification
Transformers
PyTorch
Safetensors
English
roberta
situation-entities
discourse-modes
clause-classification
narrativity
argumentation
text-embeddings-inference
Instructions to use BabakScrapes/disco-se-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BabakScrapes/disco-se-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BabakScrapes/disco-se-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BabakScrapes/disco-se-classifier") model = AutoModelForSequenceClassification.from_pretrained("BabakScrapes/disco-se-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "roberta-base", | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "BOUNDED EVENT (SPECIFIC)", | |
| "1": "BOUNDED EVENT (GENERIC)", | |
| "2": "UNBOUNDED EVENT (SPECIFIC)", | |
| "3": "UNBOUNDED EVENT (GENERIC)", | |
| "4": "BASIC STATE", | |
| "5": "COERCED STATE (SPECIFIC)", | |
| "6": "COERCED STATE (GENERIC)", | |
| "7": "PERFECT COERCED STATE (SPECIFIC)", | |
| "8": "PERFECT COERCED STATE (GENERIC)", | |
| "9": "GENERIC SENTENCE (DYNAMIC)", | |
| "10": "GENERIC SENTENCE (STATIC)", | |
| "11": "GENERIC SENTENCE (HABITUAL)", | |
| "12": "GENERALIZING SENTENCE (DYNAMIC)", | |
| "13": "GENERALIZING SENTENCE (STATIVE)", | |
| "14": "QUESTION", | |
| "15": "IMPERATIVE", | |
| "16": "NONSENSE", | |
| "17": "OTHER" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "BOUNDED EVENT (SPECIFIC)": 0, | |
| "BOUNDED EVENT (GENERIC)": 1, | |
| "UNBOUNDED EVENT (SPECIFIC)": 2, | |
| "UNBOUNDED EVENT (GENERIC)": 3, | |
| "BASIC STATE": 4, | |
| "COERCED STATE (SPECIFIC)": 5, | |
| "COERCED STATE (GENERIC)": 6, | |
| "PERFECT COERCED STATE (SPECIFIC)": 7, | |
| "PERFECT COERCED STATE (GENERIC)": 8, | |
| "GENERIC SENTENCE (DYNAMIC)": 9, | |
| "GENERIC SENTENCE (STATIC)": 10, | |
| "GENERIC SENTENCE (HABITUAL)": 11, | |
| "GENERALIZING SENTENCE (DYNAMIC)": 12, | |
| "GENERALIZING SENTENCE (STATIVE)": 13, | |
| "QUESTION": 14, | |
| "IMPERATIVE": 15, | |
| "NONSENSE": 16, | |
| "OTHER": 17 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.24.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } |