jonathanagustin's picture
Upload README.md with huggingface_hub
b19d1a8
---
language: en
license: mit
model_details: "\n ## Abstract\n This model, 'distilbert-finetuned-uncased',\
\ is a question-answering chatbot trained on the SQuAD dataset, demonstrating competency\
\ in building conversational AI using recent advances in natural language processing.\
\ It utilizes a BERT model fine-tuned for extractive question answering.\n\n \
\ ## Data Collection and Preprocessing\n The model was trained on the\
\ Stanford Question Answering Dataset (SQuAD), which contains over 100,000 question-answer\
\ pairs based on Wikipedia articles. The data preprocessing involved tokenizing\
\ context paragraphs and questions, truncating sequences to fit BERT's max length,\
\ and adding special tokens to mark question and paragraph segments.\n\n \
\ ## Model Architecture and Training\n The architecture is based on the BERT\
\ transformer model, which was pretrained on large unlabeled text corpora. For this\
\ project, the BERT base model was fine-tuned on SQuAD for extractive question answering,\
\ with additional output layers for predicting the start and end indices of the\
\ answer span.\n\n ## SQuAD 2.0 Dataset\n SQuAD 2.0 combines the existing\
\ SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers\
\ to look similar to answerable ones. This version of the dataset challenges models\
\ to not only produce answers when possible but also determine when no answer is\
\ supported by the paragraph and abstain from answering.\n "
intended_use: "\n - Answering questions from the squad_v2 dataset.\n \
\ - Developing question-answering systems within the scope of the aai520-project.\n\
\ - Research and experimentation in the NLP question-answering domain.\n\
\ "
limitations_and_bias: "\n The model inherits limitations and biases from the\
\ 'distilbert-base-uncased' model, as it was trained on the same foundational data.\n\
\ It may underperform on questions that are ambiguous or too far outside\
\ the scope of the topics covered in the squad_v2 dataset.\n Additionally,\
\ the model may reflect societal biases present in its training data.\n "
ethical_considerations: "\n This model should not be used for making critical\
\ decisions without human oversight,\n as it can generate incorrect or biased\
\ answers, especially for topics not covered in the training data.\n Users\
\ should also consider the ethical implications of using AI in decision-making processes\
\ and the potential for perpetuating biases.\n "
evaluation: "\n The model was evaluated on the squad_v2 dataset using various\
\ metrics. These metrics, along with their corresponding scores,\n are detailed\
\ in the 'eval_results' section. The evaluation process ensured a comprehensive\
\ assessment of the model's performance\n in question-answering scenarios.\n\
\ "
training: "\n The model was trained over 10 epochs with a learning rate of\
\ 2e-05, using a batch size of 64.\n The training utilized a cross-entropy\
\ loss function and the AdamW optimizer, with gradient accumulation over 4 steps.\n\
\ "
tips_and_tricks: "\n For optimal performance, questions should be clear, concise,\
\ and grammatically correct.\n The model performs best on questions related\
\ to topics covered in the squad_v2 dataset.\n It is advisable to pre-process\
\ text for consistency in encoding and punctuation, and to manage expectations for\
\ questions on topics outside the training data.\n "
model-index:
- name: distilbert-finetuned-uncased
results:
- task:
type: question-answering
dataset:
name: SQuAD v2
type: squad_v2
metrics:
- type: Exact
value: 100.0
- type: F1
value: 100.0
- type: Total
value: 2
- type: Hasans Exact
value: 100.0
- type: Hasans F1
value: 100.0
- type: Hasans Total
value: 2
- type: Best Exact
value: 100.0
- type: Best Exact Thresh
value: 0.967875599861145
- type: Best F1
value: 100.0
- type: Best F1 Thresh
value: 0.967875599861145
- type: Total Time In Seconds
value: 0.03511825300000737
- type: Samples Per Second
value: 56.9504411281387
- type: Latency In Seconds
value: 0.017559126500003686
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** mit
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data 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. -->
[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 Data Card if possible. -->
[More Information Needed]
#### 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
[More Information Needed]
#### 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]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## 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]
[More Information Needed]
## Model Card Contact
[More Information Needed]