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README.md

@@ -1,78 +1,281 @@
 ---
-tags:
-- generated_from_trainer
-datasets:
-- squad_v2
+language: en
+license: mit
+model_details: "\n        ## Abstract\n        This model, 'roberta-finetuned', 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\
+  \ 'roberta-base' 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 4 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: roberta-finetuned-squad_v2
-  results: []
+- name: roberta-finetuned
+  results:
+  - task:
+      type: question-answering
+    dataset:
+      name: SQuAD v2
+      type: squad_v2
+    metrics:
+    - type: Exact
+      value: 36.511412448412365
+    - type: F1
+      value: 39.525181841913
+    - type: Total
+      value: 11873
+    - type: Hasans Exact
+      value: 72.19973009446694
+    - type: Hasans F1
+      value: 78.23591160746174
+    - type: Hasans Total
+      value: 5928
+    - type: Noans Exact
+      value: 0.9251471825063078
+    - type: Noans F1
+      value: 0.9251471825063078
+    - type: Noans Total
+      value: 5945
+    - type: Best Exact
+      value: 50.09685841825992
+    - type: Best Exact Thresh
+      value: 0.0
+    - type: Best F1
+      value: 50.09685841825992
+    - type: Best F1 Thresh
+      value: 0.0
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Model Card for Model ID
 
-# roberta-finetuned-squad_v2
+<!-- Provide a quick summary of what the model is/does. -->
 
-This model was trained from scratch on the squad_v2 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8582
 
-## Model description
 
-More information needed
+## Model Details
 
-## Intended uses & limitations
+### Model Description
 
-More information needed
+<!-- Provide a longer summary of what this model is. -->
 
-## Training and evaluation data
 
-More information needed
 
-## Training procedure
+- **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]
 
-### Training hyperparameters
+### Model Sources [optional]
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 64
-- eval_batch_size: 64
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 256
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 4
+<!-- Provide the basic links for the model. -->
 
-### Training results
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 2.9129        | 0.2   | 100  | 1.4700          |
-| 1.4395        | 0.39  | 200  | 1.2407          |
-| 1.2356        | 0.59  | 300  | 1.0325          |
-| 1.1284        | 0.78  | 400  | 0.9750          |
-| 1.0821        | 0.98  | 500  | 0.9345          |
-| 0.9978        | 1.18  | 600  | 0.9893          |
-| 0.9697        | 1.37  | 700  | 0.9300          |
-| 0.9455        | 1.57  | 800  | 0.9351          |
-| 0.9322        | 1.76  | 900  | 0.9451          |
-| 0.9269        | 1.96  | 1000 | 0.9064          |
-| 0.9105        | 2.16  | 1100 | 0.8837          |
-| 0.8805        | 2.35  | 1200 | 0.8876          |
-| 0.8703        | 2.55  | 1300 | 0.9853          |
-| 0.8699        | 2.75  | 1400 | 0.9235          |
-| 0.8633        | 2.94  | 1500 | 0.8930          |
-| 0.828         | 3.14  | 1600 | 0.8582          |
-| 0.8284        | 3.33  | 1700 | 0.9203          |
-| 0.8076        | 3.53  | 1800 | 0.8866          |
-| 0.7805        | 3.73  | 1900 | 0.9099          |
-| 0.7974        | 3.92  | 2000 | 0.8746          |
+## 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]
 
-### Framework versions
 
-- Transformers 4.34.1
-- Pytorch 2.1.0+cu118
-- Datasets 2.14.5
-- Tokenizers 0.14.1