nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_lda_rte 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_lda_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_lda_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_lda_rte", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_lda on the GLUE RTE 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 |
|---|---|---|---|---|
| 0.7068 | 1.0 | 10 | 0.6994 | 0.4729 |
| 0.6951 | 2.0 | 20 | 0.6934 | 0.5415 |
| 0.6857 | 3.0 | 30 | 0.6938 | 0.5018 |
| 0.6671 | 4.0 | 40 | 0.7081 | 0.4946 |
| 0.6392 | 5.0 | 50 | 0.7503 | 0.5235 |
| 0.6036 | 6.0 | 60 | 0.7849 | 0.4585 |
| 0.5243 | 7.0 | 70 | 0.8591 | 0.4946 |