Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Daniel246/test4_distilbert")
model = AutoModelForSequenceClassification.from_pretrained("Daniel246/test4_distilbert")Quick Links
test4_distilbert
This model is a fine-tuned version of distilbert-base-uncased 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cpu
- Datasets 2.17.0
- Tokenizers 0.15.2
- Downloads last month
- 4
Model tree for Daniel246/test4_distilbert
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Daniel246/test4_distilbert")