Text Classification
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distilbert
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Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_20_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_20_v1_stsb")Quick Links
tiny_bert_rand_20_v1_stsb
This model is a fine-tuned version of Hartunka/tiny_bert_rand_20_v1 on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 2.2253
- Pearson: 0.1250
- Spearmanr: 0.1099
- Combined Score: 0.1174
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 |
|---|---|---|---|---|---|---|
| 3.3912 | 1.0 | 23 | 2.2253 | 0.1250 | 0.1099 | 0.1174 |
| 2.0454 | 2.0 | 46 | 2.6572 | 0.1081 | 0.0913 | 0.0997 |
| 1.8227 | 3.0 | 69 | 2.3758 | 0.1796 | 0.1638 | 0.1717 |
| 1.6031 | 4.0 | 92 | 2.4632 | 0.2419 | 0.2344 | 0.2381 |
| 1.2975 | 5.0 | 115 | 2.4302 | 0.2774 | 0.2740 | 0.2757 |
| 1.028 | 6.0 | 138 | 2.5196 | 0.2860 | 0.2863 | 0.2861 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
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Model tree for Hartunka/tiny_bert_rand_20_v1_stsb
Base model
Hartunka/tiny_bert_rand_20_v1Dataset used to train Hartunka/tiny_bert_rand_20_v1_stsb
Evaluation results
- Spearmanr on GLUE STSBself-reported0.110
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_20_v1_stsb")