Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
Indonesian
roberta
text-embeddings-inference
Instructions to use akahana/indonesia-emotion-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akahana/indonesia-emotion-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akahana/indonesia-emotion-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akahana/indonesia-emotion-roberta") model = AutoModelForSequenceClassification.from_pretrained("akahana/indonesia-emotion-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_progress_scores.csv from akahana/indonesia-emotion-roberta: direct link, hf CLI and curl.
- Browser
- Download file 715 Bytes
-
https://huggingface.co/akahana/indonesia-emotion-roberta/resolve/main/training_progress_scores.csv
- Command line
-
hf download hf://akahana/indonesia-emotion-roberta/training_progress_scores.csv
-
curl -L -o training_progress_scores.csv https://huggingface.co/akahana/indonesia-emotion-roberta/resolve/main/training_progress_scores.csv
715 Bytes
| global_step,train_loss,mcc,eval_loss | |
| 221,0.7861328125,0.5322008350854679,1.009612343528054 | |
| 442,0.08428955078125,0.6253168804343301,0.958326495777477 | |
| 663,3.033203125,0.6177684182141293,1.1226650324734775 | |
| 884,0.0022411346435546875,0.6177940443273168,1.6422297109257091 | |
| 1105,0.0008072853088378906,0.6185759700126116,1.9670504540205003 | |
| 1326,0.0001817941665649414,0.6371818915052692,1.964775475859642 | |
| 1547,0.0006866455078125,0.6373244049448687,2.0347287462516266 | |
| 1768,9.465217590332031e-05,0.6061004518366452,2.1262989894910294 | |
| 1989,0.0006518363952636719,0.6154561216565886,2.2048887759447098 | |
| 2000,0.00023237615823745728,0.6154561216565886,2.206093869426034 | |
| 2210,0.0003991127014160156,0.62709325229009,2.206396930597045 | |