stanfordnlp/snli
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How to use varun-v-rao/bart-base-lora-885K-snli-model2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="varun-v-rao/bart-base-lora-885K-snli-model2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("varun-v-rao/bart-base-lora-885K-snli-model2")
model = AutoModelForSequenceClassification.from_pretrained("varun-v-rao/bart-base-lora-885K-snli-model2", device_map="auto")This model is a fine-tuned version of facebook/bart-base on the snli 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 |
|---|---|---|---|---|
| 0.6411 | 1.0 | 2146 | 0.5055 | 0.7981 |
| 0.5713 | 2.0 | 4292 | 0.4650 | 0.8198 |
| 0.5509 | 3.0 | 6438 | 0.4485 | 0.8267 |
Base model
facebook/bart-base