Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Ftmhd/my_awesome_model")
model = AutoModelForSequenceClassification.from_pretrained("Ftmhd/my_awesome_model")Quick Links
my_awesome_model
This model is a fine-tuned version of microsoft/deberta-v3-small on an unknown dataset.
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
- Downloads last month
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Model tree for Ftmhd/my_awesome_model
Base model
microsoft/deberta-v3-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ftmhd/my_awesome_model")