Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use roncc13/trainCMDBERT-sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roncc13/trainCMDBERT-sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roncc13/trainCMDBERT-sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("roncc13/trainCMDBERT-sample") model = AutoModelForSequenceClassification.from_pretrained("roncc13/trainCMDBERT-sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from roncc13/trainCMDBERT-sample: direct link, hf CLI and curl.
- Browser
- Download file 94 Bytes
-
https://huggingface.co/roncc13/trainCMDBERT-sample/resolve/main/added_tokens.json
- Command line
-
hf download hf://roncc13/trainCMDBERT-sample/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/roncc13/trainCMDBERT-sample/resolve/main/added_tokens.json
94 Bytes
| { | |
| "[CLS]": 30002, | |
| "[MASK]": 30003, | |
| "[PAD]": 30001, | |
| "[SEP]": 30000, | |
| "[UNK]": 30004 | |
| } | |