nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_2_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_base_train_book_ent_2_stsb") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_stsb", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_2 on the GLUE STSB 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 | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 3.0836 | 1.0 | 23 | 2.4217 | 0.0699 | 0.0698 | 0.0698 |
| 2.0163 | 2.0 | 46 | 2.3733 | 0.1124 | 0.0968 | 0.1046 |
| 1.8897 | 3.0 | 69 | 2.6189 | 0.1366 | 0.1327 | 0.1346 |
| 1.6417 | 4.0 | 92 | 2.4983 | 0.1869 | 0.1827 | 0.1848 |
| 1.4223 | 5.0 | 115 | 2.6450 | 0.2115 | 0.2152 | 0.2134 |
| 1.1411 | 6.0 | 138 | 2.9155 | 0.1977 | 0.1950 | 0.1963 |
| 0.9585 | 7.0 | 161 | 2.7459 | 0.2107 | 0.2123 | 0.2115 |
Base model
distilbert/distilbert-base-uncased