nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_sst2 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_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_sst2", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s 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 |
|---|---|---|---|---|
| 0.6015 | 1.0 | 264 | 0.5511 | 0.7259 |
| 0.3516 | 2.0 | 528 | 0.5139 | 0.7787 |
| 0.2504 | 3.0 | 792 | 0.5316 | 0.7878 |
| 0.2073 | 4.0 | 1056 | 0.5494 | 0.7947 |
| 0.1786 | 5.0 | 1320 | 0.6425 | 0.7901 |
| 0.1578 | 6.0 | 1584 | 0.7026 | 0.7936 |
| 0.1405 | 7.0 | 1848 | 0.6571 | 0.8039 |
Base model
google/bert_uncased_L-4_H-512_A-8