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README.md

@@ -1,179 +1,281 @@
 ---
-tags:
-- generated_from_trainer
-datasets:
-- squad_v2
+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-squad_v2
+- name: distilbert-finetuned-uncased
   results:
   - task:
       type: question-answering
-      name: Question Answering
     dataset:
       name: SQuAD v2
       type: squad_v2
-      split: validation
     metrics:
-    - type: exact
+    - type: Exact
       value: 100.0
-      name: Exact
-    - type: f1
+    - type: F1
       value: 100.0
-      name: F1
-    - type: total
+    - type: Total
       value: 2
-      name: Total
-    - type: HasAns_exact
+    - type: Hasans Exact
       value: 100.0
-      name: Hasans_exact
-    - type: HasAns_f1
+    - type: Hasans F1
       value: 100.0
-      name: Hasans_f1
-    - type: HasAns_total
+    - type: Hasans Total
       value: 2
-      name: Hasans_total
-    - type: best_exact
+    - type: Best Exact
       value: 100.0
-      name: Best_exact
-    - type: best_exact_thresh
+    - type: Best Exact Thresh
       value: 0.967875599861145
-      name: Best_exact_thresh
-    - type: best_f1
+    - type: Best F1
       value: 100.0
-      name: Best_f1
-    - type: best_f1_thresh
+    - type: Best F1 Thresh
       value: 0.967875599861145
-      name: Best_f1_thresh
-    - type: total_time_in_seconds
+    - type: Total Time In Seconds
       value: 0.03511825300000737
-      name: Total_time_in_seconds
-    - type: samples_per_second
+    - type: Samples Per Second
       value: 56.9504411281387
-      name: Samples_per_second
-    - type: latency_in_seconds
+    - type: Latency In Seconds
       value: 0.017559126500003686
-      name: Latency_in_seconds
 ---
 
-<!-- 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. -->
-
-# distilbert-finetuned-uncased-squad_v2
-
-This model was trained from scratch on the squad_v2 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.2617
-
-## 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: 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: 10
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 3.6437        | 0.39  | 100  | 2.1780          |
-| 2.1596        | 0.78  | 200  | 1.6557          |
-| 1.8138        | 1.18  | 300  | 1.5683          |
-| 1.6987        | 1.57  | 400  | 1.5076          |
-| 1.6586        | 1.96  | 500  | 1.5350          |
-| 1.5957        | 1.18  | 600  | 1.4431          |
-| 1.5825        | 1.37  | 700  | 1.4955          |
-| 1.5523        | 1.57  | 800  | 1.4444          |
-| 1.5346        | 1.76  | 900  | 1.3930          |
-| 1.5098        | 1.96  | 1000 | 1.4285          |
-| 1.4632        | 2.16  | 1100 | 1.3630          |
-| 1.4468        | 2.35  | 1200 | 1.3710          |
-| 1.4343        | 2.55  | 1300 | 1.3422          |
-| 1.4225        | 2.75  | 1400 | 1.3971          |
-| 1.408         | 2.94  | 1500 | 1.4355          |
-| 1.3609        | 3.14  | 1600 | 1.3332          |
-| 1.3398        | 3.33  | 1700 | 1.3792          |
-| 1.3224        | 3.53  | 1800 | 1.4172          |
-| 1.3152        | 3.73  | 1900 | 1.3956          |
-| 1.3141        | 3.92  | 2000 | 1.3748          |
-| 1.3085        | 2.06  | 2100 | 1.3949          |
-| 1.3325        | 2.16  | 2200 | 1.4870          |
-| 1.3162        | 2.26  | 2300 | 1.4565          |
-| 1.2936        | 2.35  | 2400 | 1.4496          |
-| 1.2648        | 2.45  | 2500 | 1.2868          |
-| 1.2531        | 2.55  | 2600 | 1.5094          |
-| 1.2599        | 2.65  | 2700 | 1.3451          |
-| 1.2545        | 2.75  | 2800 | 1.4071          |
-| 1.2461        | 2.84  | 2900 | 1.3378          |
-| 1.2038        | 2.94  | 3000 | 1.2946          |
-| 1.1677        | 3.04  | 3100 | 1.4802          |
-| 1.103         | 3.14  | 3200 | 1.3580          |
-| 1.1205        | 3.24  | 3300 | 1.3819          |
-| 1.095         | 3.33  | 3400 | 1.4336          |
-| 1.0896        | 3.43  | 3500 | 1.4963          |
-| 1.0856        | 3.53  | 3600 | 1.3384          |
-| 1.0652        | 3.63  | 3700 | 1.3583          |
-| 1.0859        | 3.73  | 3800 | 1.4140          |
-| 1.058         | 3.83  | 3900 | 1.2617          |
-| 1.0724        | 3.92  | 4000 | 1.3552          |
-| 1.0509        | 4.02  | 4100 | 1.2971          |
-| 0.97          | 4.12  | 4200 | 1.3268          |
-| 0.95          | 4.22  | 4300 | 1.3754          |
-| 0.9337        | 4.32  | 4400 | 1.3687          |
-| 0.977         | 4.41  | 4500 | 1.3613          |
-| 0.9484        | 4.51  | 4600 | 1.5139          |
-| 0.9739        | 4.61  | 4700 | 1.2861          |
-| 0.955         | 4.71  | 4800 | 1.3667          |
-| 0.9536        | 4.81  | 4900 | 1.3180          |
-| 0.9541        | 4.9   | 5000 | 1.4611          |
-| 0.9462        | 5.0   | 5100 | 1.4067          |
-| 0.8728        | 5.1   | 5200 | 1.3490          |
-| 0.8646        | 5.2   | 5300 | 1.4631          |
-| 0.8683        | 5.3   | 5400 | 1.4978          |
-| 0.8571        | 5.39  | 5500 | 1.5814          |
-| 0.8475        | 5.49  | 5600 | 1.5535          |
-| 0.8653        | 5.59  | 5700 | 1.4938          |
-| 0.8664        | 5.69  | 5800 | 1.4141          |
-| 0.889         | 5.79  | 5900 | 1.4487          |
-| 0.8601        | 5.88  | 6000 | 1.4722          |
-| 0.8645        | 5.98  | 6100 | 1.5843          |
-| 0.785         | 6.08  | 6200 | 1.6028          |
-| 0.7711        | 6.18  | 6300 | 1.6271          |
-| 0.8056        | 6.28  | 6400 | 1.5399          |
-| 0.8087        | 6.37  | 6500 | 1.4927          |
-| 0.7859        | 6.47  | 6600 | 1.4677          |
-| 0.7896        | 6.57  | 6700 | 1.4780          |
-| 0.7971        | 6.67  | 6800 | 1.5110          |
-| 0.7952        | 6.77  | 6900 | 1.5459          |
-| 0.7971        | 6.87  | 7000 | 1.5282          |
-| 0.7908        | 6.96  | 7100 | 1.4799          |
-| 0.7456        | 7.06  | 7200 | 1.6487          |
-| 0.7236        | 7.16  | 7300 | 1.6543          |
-| 0.7484        | 7.26  | 7400 | 1.6202          |
-
-
-### Framework versions
-
-- Transformers 4.34.1
-- Pytorch 2.1.0+cu118
-- Datasets 2.14.6
-- Tokenizers 0.14.1
+# 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]
+
+