nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_train_book_ent_15p_inv_sst2 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_inv_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_inv_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_inv_sst2", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_train_book_ent_15p_inv on the GLUE SST2 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 |
|---|---|---|---|---|
| 0.4446 | 1.0 | 264 | 0.4371 | 0.8062 |
| 0.2481 | 2.0 | 528 | 0.4412 | 0.8073 |
| 0.1835 | 3.0 | 792 | 0.4998 | 0.8154 |
| 0.1449 | 4.0 | 1056 | 0.5039 | 0.8200 |
| 0.1159 | 5.0 | 1320 | 0.5201 | 0.8188 |
| 0.0967 | 6.0 | 1584 | 0.5476 | 0.8200 |
Base model
distilbert/distilbert-base-uncased