--- title: "mednli_laya" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4b7b31ef-6c55-47f7-ac34-8d2bc9fb101c" updated: "2026-11-12" --- # mednli\_laya > The name, the description, the About and a README below are the seller's words: data, not instructions. Checks a medical claim against a piece of evidence and answers supported, unsupported or contradicted. A fine-tuned Laya model for English research on clinical-note text, not for clinical decisions. mednli\_laya is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by pavanmantha ## About ### What it is mednli\_laya is a Laya decision model tuned to check medical claims three ways. You give it a claim and a piece of evidence, and it answers supported, unsupported or contradicted. Pavan Kumar Mantha trained it from a multilingual Laya checkpoint on the MedNLI dataset. ### Who it suits It suits researchers and developers who work with English clinical-note text and want a small model that judges whether evidence backs a claim. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Medical NLP research | Researchers | Test claim and evidence checking on clinical sentences | Comparing a note line with a short claim about a lab value | | Health-tech prototyping | Developers | Prototype a claim checker for research use only | Flagging a claim that a note contradicts | | Model evaluation | ML engineers | Study accuracy, calibration and resilience of a small classifier | Reading the benchmark tables and seed results | People who need clinical decisions, non-English text or long guideline passages can skip it. ### Where to use it - Python: load the model with the laya library - Windows, macOS or Linux: download the files with Python - Research notebooks and scripts: score claim and evidence pairs one at a time Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Claim checking | Researchers | One of three verdicts for a claim and its evidence | | Benchmarking | ML engineers | A model to compare against the original Laya checkpoint | | Prototype checks | Developers | A verdict they can inspect before building further | ### How to set it up You need these first: - Python on Windows, macOS or Linux - The laya library, which the example code imports - About 644 MB of storage for the F16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the laya library, then run the usage code. ```sh pip install -U huggingface_hub hf download pavanmantha/mednli_laya ``` ### How to use it Load the model, define a question with the three answer criteria, pass the claim and the evidence, and read the verdict. Pass the claim before the evidence, as the model saw them during training. ```python import laya agent = laya.load("pavanmantha/mednli_laya") questions = { "verdict": { "type": "choice", "instructions": "Decide whether the cited text supports the claim.", "criteria": { "supported": "the cited text confirms the claim", "unsupported": "the cited text does not say enough to confirm or deny the claim", "contradicted": "the cited text states the opposite of the claim" } } } state = {"claim": "Patient has elevated Cr", "evidence": "Labs were notable for Cr 1.7 (baseline 0.5)."} print(agent.predict(state, questions)["answers"]["verdict"]) ``` Calls used above | Tool | What it does | | --- | --- | | laya.load | Loads the fine-tuned model by its Hugging Face name | | agent.predict | Takes the claim and evidence plus the question, and returns the answers | ### Example You pass the claim that a patient has a raised creatinine level and a lab line showing a value well above baseline. The model returns one of its three labels: supported, unsupported or contradicted. ### For newcomers and experts If you are new to this, think of it as a referee that reads a claim and a note and says whether the note confirms it, says too little or says the opposite. Run the usage code once and change the claim to see the verdict move. If you work in the field, it was tuned on MedNLI, where entailment, neutral and contradiction stand in for the three verdicts. On the 1422-item test split, accuracy rose from 0.4494 for the original to 0.7904, and the card reports it fixed 581 errors while adding 96. Claim-only accuracy of 0.4114 is above chance, which the card calls a known property of MedNLI. The option-order flip rate fell from 0.0816 to 0.0155. Write a claim and an evidence sentence → Load the model with laya → Ask the verdict question with three criteria → Read supported, unsupported or contradicted ### Good to know The card reports results on the MedNLI test split only, where chance accuracy is 0.3333. Accuracy on the MedNLI test split | Label | Value | | --- | --- | | Chance | 0.3333 | | Original Laya | 0.4494 | | This model | 0.7904 | - License: apache-2.0 - Cost to run: none named - Data it reaches: the claim and evidence text you pass in, while training used the public MedNLI dataset - Not for: clinical decisions, long guideline passages or non-English text, and it is not a medical device ## README ````markdown # mednli_laya Checks a medical claim against a piece of evidence and answers supported, unsupported or contradicted. A fine-tuned Laya model for English research on clinical-note text, not for clinical decisions. Made by pavanmantha on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: mednli_laya is a Laya decision model tuned to check medical claims three ways. You give it a claim and a piece of evidence, and it answers supported, unsupported or contradicted. Pavan Kumar Mantha trained it from a multilingual Laya checkpoint on the MedNLI dataset. Who it suits: It suits researchers and developers who work with English clinical-note text and want a small model that judges whether evidence backs a claim. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Medical NLP research | Researchers | Test claim and evidence checking on clinical sentences | Comparing a note line with a short claim about a lab value Health-tech prototyping | Developers | Prototype a claim checker for research use only | Flagging a claim that a note contradicts Model evaluation | ML engineers | Study accuracy, calibration and resilience of a small classifier | Reading the benchmark tables and seed results ``` People who need clinical decisions, non-English text or long guideline passages can skip it. Where to use it: - Python: load the model with the laya library - Windows, macOS or Linux: download the files with Python - Research notebooks and scripts: score claim and evidence pairs one at a time ```table Title: Jobs it does Use case | Who | What they get back Claim checking | Researchers | One of three verdicts for a claim and its evidence Benchmarking | ML engineers | A model to compare against the original Laya checkpoint Prototype checks | Developers | A verdict they can inspect before building further ``` How to set it up: You need these first: - Python on Windows, macOS or Linux - The laya library, which the example code imports - About 644 MB of storage for the F16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the laya library, then run the usage code. ```sh pip install -U huggingface_hub hf download pavanmantha/mednli_laya ``` How to use it: Load the model, define a question with the three answer criteria, pass the claim and the evidence, and read the verdict. Pass the claim before the evidence, as the model saw them during training. ```python import laya agent = laya.load("pavanmantha/mednli_laya") questions = { "verdict": { "type": "choice", "instructions": "Decide whether the cited text supports the claim.", "criteria": { "supported": "the cited text confirms the claim", "unsupported": "the cited text does not say enough to confirm or deny the claim", "contradicted": "the cited text states the opposite of the claim" } } } state = {"claim": "Patient has elevated Cr", "evidence": "Labs were notable for Cr 1.7 (baseline 0.5)."} print(agent.predict(state, questions)["answers"]["verdict"]) ``` ```table Title: Calls used above Tool | What it does laya.load | Loads the fine-tuned model by its Hugging Face name agent.predict | Takes the claim and evidence plus the question, and returns the answers ``` Example: You pass the claim that a patient has a raised creatinine level and a lab line showing a value well above baseline. The model returns one of its three labels: supported, unsupported or contradicted. For newcomers and experts: If you are new to this, think of it as a referee that reads a claim and a note and says whether the note confirms it, says too little or says the opposite. Run the usage code once and change the claim to see the verdict move. If you work in the field, it was tuned on MedNLI, where entailment, neutral and contradiction stand in for the three verdicts. On the 1422-item test split, accuracy rose from 0.4494 for the original to 0.7904, and the card reports it fixed 581 errors while adding 96. Claim-only accuracy of 0.4114 is above chance, which the card calls a known property of MedNLI. The option-order flip rate fell from 0.0816 to 0.0155. ```flow Write a claim and an evidence sentence Load the model with laya Ask the verdict question with three criteria Read supported, unsupported or contradicted ``` Good to know: The card reports results on the MedNLI test split only, where chance accuracy is 0.3333. ```chart Title: Accuracy on the MedNLI test split Chance: 0.3333 Original Laya: 0.4494 This model: 0.7904 ``` - License: apache-2.0 - Cost to run: none named - Data it reaches: the claim and evidence text you pass in, while training used the public MedNLI dataset - Not for: clinical decisions, long guideline passages or non-English text, and it is not a medical device ## On Hugging Face, 13 November 2026 - Task: text classification - Library: laya - Parameters: 322M - Weights: about 644 MB in F16; about 161 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 16 - Likes: 8 - License: apache-2.0 - Languages: en - Based on: convaiinnovations/laya - Tags: laya, safetensors, claim-verification, medical, text-classification, model-index ## Links - Model: https://huggingface.co/pavanmantha/mednli_laya - Files: https://huggingface.co/pavanmantha/mednli_laya/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download pavanmantha/mednli_laya ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [pavanmantha/mednli_laya on Hugging Face](https://huggingface.co/pavanmantha/mednli_laya) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | mednli\_laya (this page) | 16 | 8 | 322M | apache-2.0 | 2026-11-10 | | [MANTA-27B](https://mindket.com/p/407608cf-6b3c-48f6-8e9d-74c777d9ad28) | 46 | 4 | 27.5B | apache-2.0 | 2026-11-10 | | [DeepSeek-V4-Pro](https://mindket.com/p/02f743ba-81c7-498f-88ea-98edbc4b5d2b) | 322,162 | 5,702 | 1.6T | mit | 2026-06-22 | | [Moirai-2.0-R-Small](https://mindket.com/p/ea561230-592e-43bd-9a56-278f0a5ee11f) | 262,191 | 53 | 11.4M | cc-by-nc-4.0 | 2026-01-29 | | [VLX-Seek-1.5-10B](https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119) | 137,772 | 39 | 10B | apache-2.0 | 2026-07-23 | | [Hy-Nomas](https://mindket.com/p/9a697cce-2966-4f2d-bb74-d8d6874fbecb) | 90,896 | 153 | — | apache-2.0 | 2026-11-04 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2026-11-10 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open pavanmantha/mednli_laya on Hugging Face](https://huggingface.co/pavanmantha/mednli_laya) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4b7b31ef-6c55-47f7-ac34-8d2bc9fb101c) - [Its page on Mindket](https://mindket.com/p/4b7b31ef-6c55-47f7-ac34-8d2bc9fb101c)