nyu-mll/glue
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How to use Hartunka/tiny_bert_rand_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_rand_10_v1_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_10_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_10_v1_stsb")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_10_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_10_v1_stsb")This model is a fine-tuned version of Hartunka/tiny_bert_rand_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 |
|---|---|---|---|---|---|---|
| 2.9227 | 1.0 | 23 | 2.3236 | 0.0790 | 0.0734 | 0.0762 |
| 1.9914 | 2.0 | 46 | 2.6350 | 0.0981 | 0.0769 | 0.0875 |
| 1.8134 | 3.0 | 69 | 2.4406 | 0.1890 | 0.1829 | 0.1859 |
| 1.6045 | 4.0 | 92 | 2.4218 | 0.2333 | 0.2319 | 0.2326 |
| 1.3303 | 5.0 | 115 | 2.5452 | 0.2553 | 0.2550 | 0.2551 |
| 1.1276 | 6.0 | 138 | 2.6353 | 0.2658 | 0.2687 | 0.2673 |
Base model
Hartunka/tiny_bert_rand_10_v1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_10_v1_stsb")