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indukurs
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albert_model

Text Classification
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albert
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="indukurs/albert_model")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("indukurs/albert_model")
model = AutoModelForSequenceClassification.from_pretrained("indukurs/albert_model", device_map="auto")
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albert_model

This model is a fine-tuned version of albert-base-v2 on the imdb dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3032
  • Accuracy: 0.9
  • F1: 0.9013

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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Base model

albert/albert-base-v2
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Dataset used to train indukurs/albert_model

Evaluation results