nyu-mll/glue
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How to use Hartunka/tiny_bert_km_10_v1_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_10_v1_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_10_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_10_v1_stsb")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_10_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_10_v1_stsb")This model is a fine-tuned version of Hartunka/tiny_bert_km_10_v1 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 |
|---|---|---|---|---|---|---|
| 3.5394 | 1.0 | 23 | 2.2270 | 0.1335 | 0.1376 | 0.1356 |
| 2.1228 | 2.0 | 46 | 2.2859 | 0.1504 | 0.1751 | 0.1628 |
| 1.9666 | 3.0 | 69 | 2.3973 | 0.1837 | 0.1858 | 0.1847 |
| 1.849 | 4.0 | 92 | 2.5343 | 0.1894 | 0.1721 | 0.1808 |
| 1.6195 | 5.0 | 115 | 2.6200 | 0.2320 | 0.2260 | 0.2290 |
| 1.4318 | 6.0 | 138 | 2.5622 | 0.2510 | 0.2461 | 0.2486 |
Base model
Hartunka/tiny_bert_km_10_v1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_10_v1_stsb")