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xsestech
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skilltext

Text2Text Generation
Transformers
Safetensors
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
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skilltext / README.md
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metadata
license: apache-2.0
base_model: google/mt5-base
tags:
  - generated_from_trainer
metrics:
  - rouge
  - bleu
model-index:
  - name: skilltext
    results: []

skilltext

This model is a fine-tuned version of google/mt5-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: nan
  • Rouge1: 0.431
  • Rouge2: 0.0
  • Rougel: 0.431
  • Rougelsum: 0.431
  • Bleu: 0.0322
  • Gen Len: 11.75

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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Bleu Gen Len
No log 0.8065 50 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 1.6129 100 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 2.4194 150 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 3.2258 200 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 4.0323 250 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 4.8387 300 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 5.6452 350 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 6.4516 400 nan 0.431 0.0 0.431 0.431 0.0322 11.75
No log 7.2581 450 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 8.0645 500 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 8.8710 550 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 9.6774 600 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 10.4839 650 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 11.2903 700 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 12.0968 750 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 12.9032 800 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 13.7097 850 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 14.5161 900 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 15.3226 950 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 16.1290 1000 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 16.9355 1050 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 17.7419 1100 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 18.5484 1150 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 19.3548 1200 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 20.1613 1250 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 20.9677 1300 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 21.7742 1350 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 22.5806 1400 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 23.3871 1450 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 24.1935 1500 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 25.0 1550 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 25.8065 1600 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 26.6129 1650 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 27.4194 1700 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 28.2258 1750 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 29.0323 1800 nan 0.431 0.0 0.431 0.431 0.0322 11.75
0.0 29.8387 1850 nan 0.431 0.0 0.431 0.431 0.0322 11.75

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.2.2
  • Datasets 2.12.0
  • Tokenizers 0.19.1