nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_15p_s_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_15p_s_stsb") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_s_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_s_stsb", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_15p_s 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 |
|---|---|---|---|---|---|---|
| 3.4421 | 1.0 | 23 | 2.2645 | nan | nan | nan |
| 2.1748 | 2.0 | 46 | 2.6349 | nan | nan | nan |
| 2.1646 | 3.0 | 69 | 2.3872 | nan | nan | nan |
| 2.1699 | 4.0 | 92 | 2.4516 | 0.0166 | 0.0134 | 0.0150 |
| 2.1567 | 5.0 | 115 | 2.3990 | nan | nan | nan |
| 2.1677 | 6.0 | 138 | 2.3650 | nan | nan | nan |
Base model
google-bert/bert-base-uncased