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bert_base_km_100_v1_stsb

Text Classification
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distilbert
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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="Hartunka/bert_base_km_100_v1_stsb")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_100_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_km_100_v1_stsb")
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bert_base_km_100_v1_stsb

This model is a fine-tuned version of Hartunka/bert_base_km_100_v1 on the GLUE STSB dataset. It achieves the following results on the evaluation set:

  • Loss: 2.2490
  • Pearson: 0.2995
  • Spearmanr: 0.2983
  • Combined Score: 0.2989

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: 256
  • eval_batch_size: 256
  • seed: 10
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Pearson Spearmanr Combined Score
2.7396 1.0 23 2.5547 0.1514 0.1351 0.1432
1.9555 2.0 46 2.3166 0.1712 0.1497 0.1605
1.749 3.0 69 2.3146 0.2127 0.2000 0.2064
1.3865 4.0 92 2.2490 0.2995 0.2983 0.2989
0.9821 5.0 115 2.7978 0.2457 0.2364 0.2410
0.6935 6.0 138 2.8239 0.2598 0.2516 0.2557
0.4947 7.0 161 2.9618 0.2405 0.2309 0.2357
0.3874 8.0 184 2.7149 0.2566 0.2501 0.2533
0.31 9.0 207 2.5269 0.2768 0.2706 0.2737

Framework versions

  • Transformers 4.50.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.21.1
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Dataset used to train Hartunka/bert_base_km_100_v1_stsb

Evaluation results