# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Mukundhan32/testmodel")
model = AutoModelForSequenceClassification.from_pretrained("Mukundhan32/testmodel")Quick Links
Contributors:
- Rohan Kamath linkedin.com/in/rohanrkamath
- Arpan Ghoshal linkedin.com/in/arpanghoshal
What is GoEmotions
Dataset labelled 58000 Reddit comments with 28 emotions
- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise + neutral
What is RoBERTa
RoBERTa builds on BERT’s language masking strategy and modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer amount of time. This allows RoBERTa representations to generalize even better to downstream tasks compared to BERT.
Hyperparameters
| Parameter | |
|---|---|
| Learning rate | 5e-5 |
| Epochs | 10 |
| Max Seq Length | 50 |
| Batch size | 16 |
| Warmup Proportion | 0.1 |
| Epsilon | 1e-8 |
Results
Best Result of Macro F1 - 49.30%
Usage
from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa")
model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa")
emotion = pipeline('sentiment-analysis',
model='arpanghoshal/EmoRoBERTa')
emotion_labels = emotion("Thanks for using it.")
print(emotion_labels)
Output
[{'label': 'gratitude', 'score': 0.9964383244514465}]
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mukundhan32/testmodel")