nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli 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_kd_complete_mnli", device_map="auto") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete on the GLUE MNLI 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 | Accuracy |
|---|---|---|---|---|
| 0.7566 | 1.0 | 1534 | 0.6799 | 0.7186 |
| 0.6322 | 2.0 | 3068 | 0.6413 | 0.7379 |
| 0.5681 | 3.0 | 4602 | 0.6223 | 0.7451 |
| 0.5157 | 4.0 | 6136 | 0.6184 | 0.7565 |
| 0.4699 | 5.0 | 7670 | 0.6115 | 0.7620 |
| 0.4266 | 6.0 | 9204 | 0.6486 | 0.7614 |
| 0.3871 | 7.0 | 10738 | 0.6570 | 0.7572 |
| 0.3532 | 8.0 | 12272 | 0.7183 | 0.7556 |
| 0.3191 | 9.0 | 13806 | 0.7695 | 0.7533 |
| 0.2903 | 10.0 | 15340 | 0.7822 | 0.7545 |
Base model
google/bert_uncased_L-4_H-512_A-8