# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("zcode/distilbert-imdb-mlflow")
model = AutoModelForSequenceClassification.from_pretrained("zcode/distilbert-imdb-mlflow")Quick Links
distilbert-imdb-mlflow
This model is a fine-tuned version of distilbert-base-cased on the imdb 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.5
Training results
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0+cu117
- Datasets 2.13.1
- Tokenizers 0.14.1
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Model tree for zcode/distilbert-imdb-mlflow
Base model
distilbert/distilbert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zcode/distilbert-imdb-mlflow")