Upload RobertaForQuestionAnswering

README.md

@@ -1,85 +1,199 @@
 ---
-language: en
-tags:
-- question-answering
-- pytorch
-- bert
-- squad
-license: mit
-datasets:
-- squad
-pipeline_tag: question-answering
-
-model-index:
-- name: roberta-large-squad_epoch_2
-  results:
-  - task: 
-      type: question-answering
-      name: Question Answering
-    metrics:
-      - type: exact_match
-        value: N/A  # You can update this with actual metrics if available
-        name: Exact Match
-      - type: f1
-        value: N/A  # You can update this with actual metrics if available
-        name: F1
-    dataset:
-      name: SQuAD
-      type: squad
-      config: plain_text  # Adding the config field
-      split: validation  # Adding the split field
+library_name: transformers
+tags: []
 ---
 
-# roberta-large-squad_epoch_2
+# Model Card for Model ID
 
-## Model description
+<!-- Provide a quick summary of what the model is/does. -->
 
-This is a fine-tuned version of [DistilBERT](https://huggingface.co/distilbert-base-cased-distilled-squad) for question answering tasks. The model was trained on SQuAD dataset.
 
-## Training procedure
 
-The model was trained with the following hyperparameters:
+## Model Details
 
-- Learning Rate: 5e-05
-- Batch Size: 8
-- Epochs: 3
-- Weight Decay: 0.01
+### Model Description
 
-## Intended uses & limitations
+<!-- Provide a longer summary of what this model is. -->
 
-This model is intended to be used for question answering tasks, particularly on SQuAD-like datasets. It performs best on factual questions where the answer can be found as a span of text within the given context.
+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:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **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
 
-The model was trained on the SQuAD dataset, which consists of questions posed by crowdworkers on a set of Wikipedia articles.
+<!-- This should link to a Dataset 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. -->
 
-### Training Hyperparameters
+[More Information Needed]
 
-The model was trained with the following hyperparameters:
+### Training Procedure
 
-* learning_rate: 5e-05
-* batch_size: 8
-* num_epochs: 3
-* weight_decay: 0.01
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-## Uses
+#### 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 Dataset 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]
 
-This model can be used for:
-- Extracting answers from text passages given questions
-- Question answering tasks
-- Reading comprehension tasks
+[More Information Needed]
 
-## Limitations
+## Model Card Authors [optional]
 
-- The model can only extract answers that are directly present in the given context
-- Performance may vary on out-of-domain texts
-- The model may struggle with complex reasoning questions
+[More Information Needed]
 
-## Additional Information
+## Model Card Contact
 
-- Model type: DistilBERT
-- Language: English
-- License: MIT
-- Framework: PyTorch
+[More Information Needed]

model.safetensors

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