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---
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
<!-- 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
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[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. -->
[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 |