gokulsrinivasagan/processed_wikitext-103-raw-v1-ld
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How to use gokulsrinivasagan/tinybert_train_book_ent_15p_ra with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="gokulsrinivasagan/tinybert_train_book_ent_15p_ra") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_ra")
model = AutoModelForMaskedLM.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_ra", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased on the gokulsrinivasagan/processed_wikitext-103-raw-v1-ld 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 |
|---|---|---|---|---|
| 3.7297 | 8.7413 | 10000 | 3.5039 | 0.4529 |
| 2.7471 | 17.4825 | 20000 | 2.6822 | 0.5690 |