Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("furyhawk/finetuning-sentiment-model-3000-samples")
model = AutoModelForSequenceClassification.from_pretrained("furyhawk/finetuning-sentiment-model-3000-samples")Quick Links
finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.2316
- Accuracy: 0.91
- F1: 0.9117
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
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Dataset used to train furyhawk/finetuning-sentiment-model-3000-samples
Evaluation results
- Accuracy on emotionself-reported0.910
- F1 on emotionself-reported0.912
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="furyhawk/finetuning-sentiment-model-3000-samples")