Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DanielViniciusAlves/outputs")
model = AutoModelForSequenceClassification.from_pretrained("DanielViniciusAlves/outputs", device_map="auto")Quick Links
outputs
This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1299
- F1: 0.7010
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: 8e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 23 | 0.2190 | 0.7611 |
| No log | 2.0 | 46 | 0.1212 | 0.2309 |
| No log | 3.0 | 69 | 0.1235 | 0.6229 |
| No log | 4.0 | 92 | 0.1299 | 0.7010 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanielViniciusAlves/outputs")