Instructions to use Guihss/TypeBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Guihss/TypeBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Guihss/TypeBERT", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Guihss/TypeBERT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "TypeBERTForSequenceClassification" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "type_bert_model.TypeBERTConfig", | |
| "AutoModelForSequenceClassification": "type_bert_model.TypeBERTForSequenceClassification" | |
| }, | |
| "id2label": { | |
| "0": "agent", | |
| "1": "event", | |
| "2": "place", | |
| "3": "item", | |
| "4": "virtual", | |
| "5": "concept" | |
| }, | |
| "label2id": { | |
| "agent": 0, | |
| "concept": 5, | |
| "event": 1, | |
| "item": 3, | |
| "place": 2, | |
| "virtual": 4 | |
| }, | |
| "model_type": "type_bert", | |
| "tokenizer_class": "bert-base-uncased", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.22.1" | |
| } | |