Text Classification
Transformers
Safetensors
English
bert
arxiv
scientific-text-classification
scibert
streamlit-demo
text-embeddings-inference
Instructions to use Ian-Khalzov/article-topic-service-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ian-Khalzov/article-topic-service-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ian-Khalzov/article-topic-service-scibert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ian-Khalzov/article-topic-service-scibert") model = AutoModelForSequenceClassification.from_pretrained("Ian-Khalzov/article-topic-service-scibert") - 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, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Artificial Intelligence", | |
| "1": "Natural Language Processing", | |
| "2": "Computer Vision", | |
| "3": "Machine Learning", | |
| "4": "Computer Science Theory and Algorithms", | |
| "5": "Mathematics", | |
| "6": "Statistics", | |
| "7": "Electrical Engineering", | |
| "8": "Astrophysics", | |
| "9": "Condensed Matter Physics", | |
| "10": "Quantum Physics", | |
| "11": "Quantitative Biology" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "Artificial Intelligence": 0, | |
| "Astrophysics": 8, | |
| "Computer Science Theory and Algorithms": 4, | |
| "Computer Vision": 2, | |
| "Condensed Matter Physics": 9, | |
| "Electrical Engineering": 7, | |
| "Machine Learning": 3, | |
| "Mathematics": 5, | |
| "Natural Language Processing": 1, | |
| "Quantitative Biology": 11, | |
| "Quantum Physics": 10, | |
| "Statistics": 6 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.5.0", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 31090 | |
| } | |