Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sofiane-isi/finetuning-sentiment-model-3000-samples")
model = AutoModelForSequenceClassification.from_pretrained("sofiane-isi/finetuning-sentiment-model-3000-samples", device_map="auto")Quick Links
finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.6987
- eval_model_preparation_time: 0.0016
- eval_accuracy: 0.5
- eval_f1: 0.6667
- eval_runtime: 7.1135
- eval_samples_per_second: 42.173
- eval_steps_per_second: 2.671
- epoch: 0
- step: 0
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: linear
- num_epochs: 2
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1
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Model tree for sofiane-isi/finetuning-sentiment-model-3000-samples
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sofiane-isi/finetuning-sentiment-model-3000-samples")