Instructions to use bradmin/rm_trl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bradmin/rm_trl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bradmin/rm_trl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bradmin/rm_trl") model = AutoModelForSequenceClassification.from_pretrained("bradmin/rm_trl") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_lower_case": false, | |
| "do_basic_tokenize": true, | |
| "never_split": null, | |
| "unk_token": "[UNK]", | |
| "sep_token": "[SEP]", | |
| "pad_token": "[PAD]", | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "bos_token": "[CLS]", | |
| "eos_token": "[SEP]", | |
| "tokenize_chinese_chars": true, | |
| "strip_accents": null, | |
| "model_max_length": 512, | |
| "tokenizer_class": "BertTokenizer" | |
| } | |