nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in_mrpc 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_a_in_mrpc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in_mrpc")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in_mrpc", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in on the GLUE MRPC 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 | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.6214 | 1.0 | 15 | 0.5932 | 0.7010 | 0.8100 | 0.7555 |
| 0.5853 | 2.0 | 30 | 0.5762 | 0.7157 | 0.8204 | 0.7681 |
| 0.5515 | 3.0 | 45 | 0.5727 | 0.7255 | 0.8199 | 0.7727 |
| 0.5201 | 4.0 | 60 | 0.5971 | 0.6985 | 0.7776 | 0.7381 |
| 0.4485 | 5.0 | 75 | 0.6437 | 0.6667 | 0.7527 | 0.7097 |
| 0.3653 | 6.0 | 90 | 0.6802 | 0.7010 | 0.7967 | 0.7488 |
| 0.2927 | 7.0 | 105 | 0.7607 | 0.6814 | 0.7610 | 0.7212 |
| 0.232 | 8.0 | 120 | 0.8599 | 0.7108 | 0.8007 | 0.7557 |
Base model
google/bert_uncased_L-4_H-512_A-8