metadata

language:
  - en
license: mit
library_name: transformers
datasets:
  - fedora-copr/autoannotated_snippets_mistral
metrics:
  - rouge
tags:
  - code
model_index:
  name: phi-2-snippets-logdetective
  results:
    - task:
        type: text-generation
      dataset:
        type: fedora-copr/autoannotated_snippets_mistral
        name: autoannotated_snippets_mistral
      metrics:
        - name: rouge-1-recall
          type: rouge-1
          value: 0.4928060294187831
          verified: false
        - name: rouge-1-precision
          type: rouge-1
          value: 0.3842279864863966
          verified: false
        - name: rouge-1-f1
          type: rouge-1
          value: 0.4228375247665276
          verified: false
        - name: rouge-2-recall
          type: rouge-2
          value: 0.22104701377745636
          verified: false
        - name: rouge-2-precision
          type: rouge-2
          value: 0.15216741180621804
          verified: false
        - name: rouge-2-f1
          type: rouge-2
          value: 0.17506785950227427
          verified: false
        - name: rouge-l-recall
          type: rouge-l
          value: 0.4588693388086414
          verified: false
        - name: rouge-l-precision
          type: rouge-l
          value: 0.3579633500466938
          verified: false
        - name: rouge-l-f1
          type: rouge-l
          value: 0.3938760006165079
          verified: false

Model Card for Model ID

Model Details

Model Description

Developed by: Jiri Podivin jpodivin@redhat.com
Model type: phi-2
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: microsoft/phi-2

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

fedora-copr/autoannotated_snippets_mistral

Factors

[More Information Needed]

Metrics

Rouge metric was used to compare model outputs with expected annotations from test subset.

Results

[More Information Needed]

Summary

Technical Specifications

Compute Infrastructure

Single node

Hardware

1 * GeForce RTX 4090

Software

transformers
peft

Model Card Authors [optional]

Jiri Podivin jpodivin@redhat.com