---
library_name: transformers
license: apache-2.0
datasets:
- MENG21/studfacultyeval_dataset
language:
- tl
- en
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: text-classification
widget:
- text: laging pumapasok si sir!
  example_title: Tagalog
- text: I really don't understand the lesson
  example_title: English
- text: I love the way na magturo si sir, I always understand the lesson lagi
  example_title: Tag-lish
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Allan O. Ibo, Jr.
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- **Model type:** Text Classification
- **Language(s) (NLP):** English/Tagalog
- **License:** Apache License
- **Finetuned from model [optional]:** BERT (Large)

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [User Guide](https://huggingface.co/spaces/MENG21/studfacultyeval_prototype/blob/main/prototype-user-guide.pdf)

## Uses

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### 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

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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

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## Training Details

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# results_bert-large-uncased

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2128
- Accuracy: 0.9141
- Precision: 0.9182
- Recall: 0.9421
- F1: 0.9300

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6415        | 0.09  | 50   | 0.5315          | 0.7175   | 0.6981    | 0.9394 | 0.8010 |
| 0.4007        | 0.18  | 100  | 0.7702          | 0.7243   | 0.9892    | 0.5505 | 0.7074 |
| 0.5158        | 0.28  | 150  | 0.4075          | 0.8591   | 0.8904    | 0.8748 | 0.8825 |
| 0.3934        | 0.37  | 200  | 0.2809          | 0.8763   | 0.9354    | 0.8546 | 0.8932 |
| 0.2691        | 0.46  | 250  | 0.3406          | 0.8832   | 0.8837    | 0.9294 | 0.9060 |
| 0.2814        | 0.55  | 300  | 0.2582          | 0.8768   | 0.8512    | 0.9651 | 0.9046 |
| 0.2735        | 0.64  | 350  | 0.2715          | 0.8953   | 0.8708    | 0.9711 | 0.9182 |
| 0.2411        | 0.74  | 400  | 0.2389          | 0.9103   | 0.9242    | 0.9279 | 0.9260 |
| 0.2371        | 0.83  | 450  | 0.2081          | 0.9104   | 0.9212    | 0.9316 | 0.9264 |
| 0.1974        | 0.92  | 500  | 0.2128          | 0.9141   | 0.9182    | 0.9421 | 0.9300 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2

<!-- <###################################################################> -->

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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