README.md

---
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

<!-- Provide a quick summary of what the model is/does. -->

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Jiri Podivin <[email protected]>
- **Model type:** phi-2
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** microsoft/phi-2


### Model Sources [optional]

<!-- Provide the basic links for the model. -->

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

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[fedora-copr/autoannotated_snippets_mistral](https://huggingface.co/datasets/fedora-copr/autoannotated_snippets_mistral)

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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 <[email protected]>