Cheng98 / llama-160m-boolq

huggingface.co
Total runs: 6
24-hour runs: 0
7-day runs: -2
30-day runs: -7
Model's Last Updated: April 08 2024
text-classification

Introduction of llama-160m-boolq

Model Details of llama-160m-boolq

llama-160m-boolq

This model is a fine-tuned version of JackFram/llama-160m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6795
  • Accuracy: 0.5957
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: 16
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4
Training results
Training Loss Epoch Step Validation Loss Accuracy
No log 0.99 73 0.6870 0.5731
No log 1.99 147 0.6825 0.5957
No log 3.0 221 0.6809 0.6012
No log 3.96 292 0.6795 0.5957
Framework versions
  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.18.0
  • Tokenizers 0.13.3

Runs of Cheng98 llama-160m-boolq on huggingface.co

6
Total runs
0
24-hour runs
0
3-day runs
-2
7-day runs
-7
30-day runs

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https://huggingface.co/Cheng98/llama-160m-boolq

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Cheng98 llama-160m-boolq online free url in huggingface.co:

https://huggingface.co/Cheng98/llama-160m-boolq

llama-160m-boolq install

llama-160m-boolq is an open source model from GitHub that offers a free installation service, and any user can find llama-160m-boolq on GitHub to install. At the same time, huggingface.co provides the effect of llama-160m-boolq install, users can directly use llama-160m-boolq installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

llama-160m-boolq install url in huggingface.co:

https://huggingface.co/Cheng98/llama-160m-boolq

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