nyu-mll/glue
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How to use goktug14/gpt1_sst2_left with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="goktug14/gpt1_sst2_left") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("goktug14/gpt1_sst2_left")
model = AutoModelForSequenceClassification.from_pretrained("goktug14/gpt1_sst2_left")This model is a fine-tuned version of openai-gpt on sst2 dataset of GLUE benchmark. It achieves the following results on the evaluation set:
Access to Repository for finetuning.
More information needed
More information needed
For batched training, <pad> token is added to the tokenizer and the following padding-truncation options are adapted:
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision |
|---|---|---|---|---|---|---|
| 0.2083 | 1.0 | 4210 | 0.2243 | 0.9266 | 0.9279 | 0.9279 |
| 0.1495 | 2.0 | 8420 | 0.3193 | 0.9300 | 0.9505 | 0.9154 |
| 0.0859 | 3.0 | 12630 | 0.3456 | 0.9255 | 0.9369 | 0.9183 |
| 0.0605 | 4.0 | 16840 | 0.4150 | 0.9266 | 0.9437 | 0.9148 |
Base model
openai-community/openai-gpt