Text Classification
Transformers
Safetensors
English
deberta-v2
feature-extraction
ielts
automated-essay-scoring
deberta-v3
regression
nlp
custom_code
text-embeddings-inference
Instructions to use star092304/ielts-writing-task2-debertav3base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use star092304/ielts-writing-task2-debertav3base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="star092304/ielts-writing-task2-debertav3base", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("star092304/ielts-writing-task2-debertav3base", trust_remote_code=True) model = AutoModel.from_pretrained("star092304/ielts-writing-task2-debertav3base", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e8deedba2b0af559ef1038562d0c14a19b10ee57e262d58a508fd2390f59c4a
- Size of remote file:
- 22.7 MB
- SHA256:
- 2b8b6b88c175b1b39642fba3ad7336b8165b8c8c1b061de2116f8f64422d5dd1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.