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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_history.csv from star092304/ielts-writing-task2-debertav3base: direct link, hf CLI and curl.
- Browser
- Download file 492 Bytes
-
https://huggingface.co/star092304/ielts-writing-task2-debertav3base/resolve/main/training_history.csv
- Command line
-
hf download hf://star092304/ielts-writing-task2-debertav3base/training_history.csv
-
curl -L -o training_history.csv https://huggingface.co/star092304/ielts-writing-task2-debertav3base/resolve/main/training_history.csv
492 Bytes
| epoch,train_loss,TA,CC,LR,GRA,OverallBand,avg_qwk,val_loss | |
| 1,0.042457533743399514,0.3564,0.338,0.5395,0.6175,0.4654,0.4634,0.03586 | |
| 2,0.026793291574542383,0.5092,0.4707,0.6512,0.7061,0.6022,0.5879,0.0284 | |
| 3,0.02471122434183812,0.5824,0.5452,0.7001,0.7378,0.6509,0.6433,0.02702 | |
| 4,0.022730398019798117,0.6032,0.5727,0.7023,0.7462,0.6671,0.6583,0.02651 | |
| 5,0.02116386474276467,0.525,0.4968,0.6399,0.6876,0.5933,0.5885,0.03349 | |
| 6,0.019647491585986734,0.5396,0.5142,0.6452,0.6987,0.6057,0.6007,0.03209 | |