Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("orpe42/deberta_v3_MP_ft")
model = AutoModelForSequenceClassification.from_pretrained("orpe42/deberta_v3_MP_ft", device_map="auto")Quick Links
deberta_v3_MP_ft
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0218
- Macro F1: 0.0068
- Micro F1: 0.0236
- Macro Precision: 0.1547
- Macro Recall: 0.0037
- Micro Precision: 0.8725
- Micro Recall: 0.0120
- Exact Match Ratio: 0.0164
- Macro Roc Auc: 0.7865
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Macro Precision | Macro Recall | Micro Precision | Micro Recall | Exact Match Ratio | Macro Roc Auc |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0287 | 6.5789 | 500 | 0.0227 | 0.0025 | 0.0084 | 0.1303 | 0.0013 | 0.8873 | 0.0042 | 0.0059 | 0.7649 |
| 0.0277 | 10.0 | 760 | 0.0218 | 0.0062 | 0.0220 | 0.1611 | 0.0033 | 0.8872 | 0.0111 | 0.0058 | 0.7843 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.5.1+cu121
- Datasets 5.0.1
- Tokenizers 0.22.2
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Model tree for orpe42/deberta_v3_MP_ft
Base model
microsoft/deberta-v3-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="orpe42/deberta_v3_MP_ft")