Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("satpalsr/de-beginning")
model = AutoModelForSequenceClassification.from_pretrained("satpalsr/de-beginning")Quick Links
reward-model-out
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.6273
- eval_accuracy: 0.6449
- eval_precision: 0.6600
- eval_recall: 0.8789
- eval_f1: 0.7539
- eval_runtime: 110.7892
- eval_samples_per_second: 35.536
- eval_steps_per_second: 8.891
- epoch: 0.51
- step: 600
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: 6e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
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Model tree for satpalsr/de-beginning
Base model
microsoft/deberta-v3-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="satpalsr/de-beginning")