Model save

README.md

@@ -1,281 +1,78 @@
 ---
-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        "
+tags:
+- generated_from_trainer
+datasets:
+- squad_v2
 model-index:
-- 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
+- name: roberta-finetuned-squad_v2
+  results: []
 ---
 
-# Model Card for Model ID
+<!-- 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. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+# roberta-finetuned-squad_v2
 
+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
 
-## Model Details
+More information needed
 
-### Model Description
+## Intended uses & limitations
 
-<!-- Provide a longer summary of what this model is. -->
+More information needed
 
+## Training and evaluation data
 
+More information needed
 
-- **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 procedure
 
-### Model Sources [optional]
+### Training hyperparameters
 
-<!-- Provide the basic links for the model. -->
+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
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Training results
 
-## Uses
+| 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          |
 
-<!-- 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

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/content/drive/My Drive/Colab Notebooks/aai520-project/checkpoints/roberta-finetuned/checkpoint-1000",
+  "_name_or_path": "/content/drive/My Drive/Colab Notebooks/aai520-project/checkpoints/roberta-finetuned/checkpoint-2000",
   "architectures": [
     "RobertaForQuestionAnswering"
   ],

metrics.json

@@ -1,12 +1,12 @@
 {
-    "exact": 37.40419439063421,
-    "f1": 40.42817816263742,
+    "exact": 36.511412448412365,
+    "f1": 39.525181841913,
     "total": 11873,
-    "HasAns_exact": 72.58771929824562,
-    "HasAns_f1": 78.64435886049151,
+    "HasAns_exact": 72.19973009446694,
+    "HasAns_f1": 78.23591160746174,
     "HasAns_total": 5928,
-    "NoAns_exact": 2.3212783851976453,
-    "NoAns_f1": 2.3212783851976453,
+    "NoAns_exact": 0.9251471825063078,
+    "NoAns_f1": 0.9251471825063078,
     "NoAns_total": 5945,
     "best_exact": 50.09685841825992,
     "best_exact_thresh": 0.0,

tokenizer.json

@@ -3,13 +3,11 @@
   "truncation": {
     "direction": "Right",
     "max_length": 512,
-    "strategy": "LongestFirst",
-    "stride": 0
+    "strategy": "OnlySecond",
+    "stride": 128
   },
   "padding": {
-    "strategy": {
-      "Fixed": 512
-    },
+    "strategy": "BatchLongest",
     "direction": "Right",
     "pad_to_multiple_of": null,
     "pad_id": 1,

trainer_state.json

@@ -1,9 +1,9 @@
 {
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-  "best_model_checkpoint": "/content/drive/My Drive/Colab Notebooks/aai520-project/checkpoints/roberta-finetuned/checkpoint-1000",
-  "epoch": 1.9607843137254903,
+  "best_metric": 0.8582048416137695,
+  "best_model_checkpoint": "/content/drive/My Drive/Colab Notebooks/aai520-project/checkpoints/roberta-finetuned/checkpoint-1600",
+  "epoch": 4.0,
   "eval_steps": 100,
-  "global_step": 1000,
+  "global_step": 2040,
   "is_hyper_param_search": false,
   "is_local_process_zero": true,
   "is_world_process_zero": true,
@@ -149,28 +149,168 @@
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 }