nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_train_book_ent_15p_mrpc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/tinybert_train_book_ent_15p_mrpc") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_mrpc")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_mrpc", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_train_book_ent_15p 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 |
|---|---|---|---|---|---|---|
| 0.6206 | 1.0 | 15 | 0.6060 | 0.7010 | 0.8100 | 0.7555 |
| 0.5925 | 2.0 | 30 | 0.5914 | 0.7059 | 0.8101 | 0.7580 |
| 0.5718 | 3.0 | 45 | 0.5963 | 0.7108 | 0.8207 | 0.7657 |
| 0.5525 | 4.0 | 60 | 0.6276 | 0.6618 | 0.7518 | 0.7068 |
| 0.5074 | 5.0 | 75 | 0.6395 | 0.6716 | 0.7674 | 0.7195 |
| 0.4444 | 6.0 | 90 | 0.7470 | 0.625 | 0.7151 | 0.6700 |
| 0.3636 | 7.0 | 105 | 0.8653 | 0.6054 | 0.6979 | 0.6517 |
Base model
distilbert/distilbert-base-uncased