---
language: en
tags:
- text-classification
tasks:
- text-classification
dataset_name: New
---

# Model Card for acuvity/model_integration_test

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



## Model Details

### Model Description

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

Auto Fine-tuned acuvity/model_integration_test for text-classification task. The run id is v0.0.4

- **Developed by:** Auto-Finetune Bot
- **Funded by [optional]:** Auto-Finetune Bot
- **Shared by [optional]:** Auto-Finetune Bot
- **Model type:** text-classification
- **Language(s) (NLP):** en
- **License:** Closed Source
- **Finetuned from model [optional]:** acuvity/model_integration_test

### Model Sources [optional]

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

- **Repository:** [acuvity/model_integration_test](https://huggingface.co/acuvity/acuvity/model_integration_test)
- **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.


```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("acuvity/model_integration_test")
model = AutoModelForSequenceClassification.from_pretrained("acuvity/model_integration_test")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]
```
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="acuvity/model_integration_test")
classifier("Hello, my dog is cute")
```
    

## 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. -->

(New | v0.0.4) [https://huggingface.co/datasets/acuvity/New]

### 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]

No modifications done on the dataset.


#### Training Hyperparameters

- **Training regime:** {'fp16_bool': False, 'num_train_epochs': 5, 'learning_rate': 1e-05, 'batch_size': 256, 'weight_decay': 0.01} <!--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. -->

(New | v0.0.4) [https://huggingface.co/datasets/acuvity/New]

#### 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. -->

[More Information Needed]

### Results


# Auto Finetune Report for Prompt Injection
## Model URL: acuvity/model_integration_test
## Model Commit: v0.0.4

## Quick Summary

Accuracy: 0.0008237232289950436
Regression: 0.0006873789967815756
Improvement: 0.0009149276196107874

## Results Summary

### Prompt Injection | v0.0.4 Results

|          |   accuracy |        f1 |   precision |   recall |
|:---------|-----------:|----------:|------------:|---------:|
| New      |  0.999176  | 0.998993  |   1         | 0.997988 |
| Baseline |  0.999126  | 0.998993  |   1         | 0.997988 |
| Feedback |  1         | 0         |   0         | 0        |
| QA       |  0.982216  | 0         |   0         | 0        |
| PINT     |  0.0701696 | 0.0790514 |   0.0421793 | 0.628272 |
| Sanity   |  0.630435  | 0.773333  |   0.630435  | 1        |
----------------------------------------------------
### Prompt Injection | v0.0.3 Results
|          |   accuracy |        f1 |   precision |   recall |
|:---------|-----------:|----------:|------------:|---------:|
| New      |  0.998353  | 0.997988  |   1         | 0.995984 |
| Baseline |  0.998252  | 0.997988  |   1         | 0.995984 |
| Feedback |  1         | 0         |   0         | 0        |
| QA       |  0.98164   | 0         |   0         | 0        |
| PINT     |  0.0705022 | 0.0808944 |   0.0432337 | 0.627551 |
| Sanity   |  0.630435  | 0.773333  |   0.630435  | 1        |

    

#### Summary



## Model Examination [optional]

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

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon 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).

- **Hardware Type:** Quadro P4000
- **Hours used:** 4 Hours
- **Cloud Provider:** Paperspace | Digital Ocean
- **Compute Region:** NY2
- **Carbon Emitted:** 0, we are carbon neutral

## Technical Specifications [optional]

### Model Architecture and Objective

acuvity/model_integration_test

### Compute Infrastructure

|                  | 0                                            |
|:-----------------|:---------------------------------------------|
| platform         | Linux                                        |
| platform-release | 5.15.0-130-generic                           |
| platform-version | #140-Ubuntu SMP Wed Dec 18 17:59:53 UTC 2024 |
| architecture     | x86_64                                       |
| processor        | x86_64                                       |
| ram              | 29 GB                                        |

#### Hardware

Quadro P4000

#### Software

|                      | 0           |
|:---------------------|:------------|
| python_version       | 3.10.16     |
| pytorch_version      | 2.5.1+cu124 |
| transformers_version | 4.47.1      |
| datasets_version     | 3.2.0       |

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

acuvity

## Model Card Contact

[acuvity@acuvity.ai](mailto:acuvity@acuvity.ai)