nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_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_init_kd_complete_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_sst2")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete 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 |
|---|---|---|---|---|
| 0.3388 | 1.0 | 264 | 0.3645 | 0.8601 |
| 0.2201 | 2.0 | 528 | 0.3685 | 0.8612 |
| 0.1649 | 3.0 | 792 | 0.4037 | 0.8612 |
| 0.1329 | 4.0 | 1056 | 0.4017 | 0.8761 |
| 0.1088 | 5.0 | 1320 | 0.3973 | 0.875 |
| 0.094 | 6.0 | 1584 | 0.4348 | 0.8658 |
Base model
google/bert_uncased_L-4_H-512_A-8