nyu-mll/glue
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How to use Hartunka/tiny_bert_rand_10_v2_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_10_v2_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_10_v2_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_10_v2_stsb")This model is a fine-tuned version of Hartunka/tiny_bert_rand_10_v2 on the GLUE STSB dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 3.1623 | 1.0 | 23 | 2.2278 | 0.1029 | 0.1103 | 0.1066 |
| 2.0283 | 2.0 | 46 | 2.6435 | 0.0808 | 0.0590 | 0.0699 |
| 1.8414 | 3.0 | 69 | 2.4582 | 0.1896 | 0.1824 | 0.1860 |
| 1.6228 | 4.0 | 92 | 2.4701 | 0.2441 | 0.2441 | 0.2441 |
| 1.3269 | 5.0 | 115 | 2.4297 | 0.2678 | 0.2654 | 0.2666 |
| 1.0809 | 6.0 | 138 | 2.4510 | 0.2983 | 0.3000 | 0.2992 |
Base model
Hartunka/tiny_bert_rand_10_v2