---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
metrics:
- recall
- precision
- f1
pipeline_tag: text-generation
datasets:
- yvelos/semantic_annotation
license: apache-2.0
---

# Annotator_1_Mi

## Overview
  Annotator_1_Mi is the First LLM for semantic tabular data annotation
  
## Model Details

### Model Description
Annotator_1_Mi is a Decoder-based LM fine-tuned from [Mistravl-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)


- **Developed by:** [tsotsa](https://github.com/jiofidelus/tsotsa)
- **Model type:** Decoder
- **Language(s) (NLP):** Semantic annotation for tabular data
- **License:** Apache 2.0
- **Finetuned from model:** [Mistravl-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)

### Licence
Annotator_1_Mi is developed under Apache 2.0 licence

## Uses

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### 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

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[More Information Needed]

### Recommendations

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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

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[More Information Needed]

### Training Procedure 

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#### 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. -->

[More Information Needed]

#### Factors

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

#### Metrics
- **Recall**
- **Precision**
- **F1 Score**


### Results

[More Information Needed]

#### Summary


## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact
bon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Environment:** Google collab
- **GPU Type:** T4 with 15 Go
- **Hours used:** 100.4 min

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

**BibTeX:**

[More Information Needed]

**APA:**


## Model Card Contact

**[tsotsa](jeanpetityvelos@gmail.com)**


### Framework versions

- PEFT 0.8.2