nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_lda_50_v1_book_mnli with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_base_lda_50_v1_book_mnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_lda_50_v1_book_mnli")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_lda_50_v1_book_mnli")This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_50_v1_book on the GLUE MNLI 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 |
|---|---|---|---|---|
| 0.695 | 1.0 | 1534 | 0.5630 | 0.7743 |
| 0.4907 | 2.0 | 3068 | 0.5236 | 0.7984 |
| 0.3874 | 3.0 | 4602 | 0.5264 | 0.8022 |
| 0.2906 | 4.0 | 6136 | 0.6159 | 0.8029 |
| 0.2185 | 5.0 | 7670 | 0.6220 | 0.8014 |
| 0.1658 | 6.0 | 9204 | 0.7832 | 0.8 |
| 0.1301 | 7.0 | 10738 | 0.7668 | 0.8019 |
Base model
gokulsrinivasagan/bert_base_lda_50_v1_book