nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book_cola", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book on the GLUE COLA 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 | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6062 | 1.0 | 34 | 0.5959 | 0.0464 | 0.6922 |
| 0.5592 | 2.0 | 68 | 0.5788 | 0.1742 | 0.7018 |
| 0.4854 | 3.0 | 102 | 0.5999 | 0.2699 | 0.7267 |
| 0.4222 | 4.0 | 136 | 0.6377 | 0.2809 | 0.7267 |
| 0.3559 | 5.0 | 170 | 0.6354 | 0.3327 | 0.7383 |
| 0.3018 | 6.0 | 204 | 0.7614 | 0.3084 | 0.7363 |
| 0.2575 | 7.0 | 238 | 0.8172 | 0.3191 | 0.7392 |
Base model
google/bert_uncased_L-4_H-512_A-8