Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cduncanja/emotion_model")
model = AutoModelForSequenceClassification.from_pretrained("cduncanja/emotion_model")Quick Links
emotion_model
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 1.7815
- F1: 0.1455
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 1.7968 | 1.0 | 2 | 1.7804 | 0.2286 |
| 1.7918 | 2.0 | 4 | 1.7812 | 0.2286 |
| 1.7867 | 3.0 | 6 | 1.7822 | 0.08 |
| 1.7884 | 4.0 | 8 | 1.7816 | 0.08 |
| 1.7833 | 5.0 | 10 | 1.7815 | 0.1455 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.11.0
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Dataset used to train cduncanja/emotion_model
Evaluation results
- F1 on emotionself-reported0.145
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cduncanja/emotion_model")