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---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
---
Description: Do the sentences have the same meaning?\
Original dataset: https://huggingface.co/datasets/glue/viewer/mrpc \
---\
Try querying this adapter for free in Lora Land at https://predibase.com/lora-land! \
The adapter_category is Academic Benchmarks and the name is Sentence Comparison (MRPC)\
---\
Sample input: You are given two sentences below, Sentence 1 and Sentence 2. If the two sentences are semantically equivalent, please return 1. Otherwise, please return 0.\n\n### Sentence 1: The association said 28.2 million DVDs were rented in the week that ended June 15 , compared with 27.3 million VHS cassettes .\n\n### Sentence 2: The Video Software Dealers Association said 28.2 million DVDs were rented out last week , compared to 27.3 million VHS cassettes .\n\n### Label: \
---\
Sample output: 1\
---\
Try using this adapter yourself!
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mistral-7B-v0.1"
peft_model_id = "predibase/glue_mrpc"
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)
``` |