Instructions to use OliverHeine/albert-base-v2_fold_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/albert-base-v2_fold_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/albert-base-v2_fold_7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/albert-base-v2_fold_7") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/albert-base-v2_fold_7") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": true, | |
| "eos_token": "[SEP]", | |
| "is_local": false, | |
| "keep_accents": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "AlbertTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |