| ---\ | |
| library_name: peft\ | |
| Base model: mistralai/Mistral-7B-v0.1\ | |
| pipeline_tag: text-generation\ | |
| ---\ | |
| Description: Does the hypothesis entail the premise?\ | |
| Original dataset: glue_mnli \ | |
| ---\ | |
| 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 Natural Language Inference (MNLI)\ | |
| ---\ | |
| Sample input: You are given a premise and a hypothesis below. If the premise entails the hypothesis, return 0. If the premise contradicts the hypothesis, return 2. Otherwise, if the premise does neither, return 1.\n\n### Premise: You and your friends are not welcome here, said Severn.\n\n### Hypothesis: Severn said the people were not welcome there.\n\n### Label: \ | |
| ---\ | |
| Sample output: 0\ | |
| ---\ | |
| Try using this adapter yourself! | |
| ``` | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_id = "mistralai/Mistral-7B-v0.1" | |
| peft_model_id = "predibase/glue_mnli" | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| model.load_adapter(peft_model_id) | |
| ``` |