Text Classification
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deberta-v2
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("MaVier19/zero-shot_text_classification")
model = AutoModelForSequenceClassification.from_pretrained("MaVier19/zero-shot_text_classification", device_map="auto")Quick Links
zero-shot_text_classification
This model is a fine-tuned version of MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6793
- Accuracy: 0.7785
- F1: 0.7798
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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.9575 | 1.0 | 1000 | 0.6793 | 0.7785 | 0.7798 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for MaVier19/zero-shot_text_classification
Base model
MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MaVier19/zero-shot_text_classification")