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stud-fac-eval-bert-large-uncased_v2

Text Classification
Transformers
Safetensors
Tagalog
English
bert
text-embeddings-inference
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 ---
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
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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.
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
- **Model type:** Text Classification
- **Language(s) (NLP):** English/Tagalog
- **License:** Apache License
- **Finetuned from model [optional]:** BERT (Large)
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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
<!-- 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
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[More Information Needed]
### Downstream Use [optional]
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### Out-of-Scope Use
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[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
<!-- <###################################################################> -->
# 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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[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
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### 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:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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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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