| ---\ | |
| library_name: peft\ | |
| Base model: mistralai/Mistral-7B-v0.1\ | |
| pipeline_tag: text-generation\ | |
| ---\ | |
| Description: How similar are the sentences?\ | |
| Original dataset: glue_stsb \ | |
| ---\ | |
| 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 Similarity (STSB)\ | |
| ---\ | |
| Sample input: You are given two sentences below, Sentence 1 and Sentence 2. Please determine, on a scale from 0 to 5, with 0 being least similar and 5 being most similar, how similar the two sentences are:\n\n### Sentence 1: A woman peels a potato.\n\n### Sentence 2: A woman is peeling a potato.\n\n### Similarity Score: \ | |
| ---\ | |
| Sample output: 4.8\ | |
| ---\ | |
| Try using this adapter yourself! | |
| ``` | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_id = "mistralai/Mistral-7B-v0.1" | |
| peft_model_id = "predibase/glue_stsb" | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| model.load_adapter(peft_model_id) | |
| ``` |