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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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train_df['emotion_stance'] = "Classify based on the features:" + train_df['target_emotion_stance'].apply(lambda x: str(x)) + " in the text: " + train_df['source_text'] |
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train_df['emotion'] = "Classify based on the features:" + train_df['target_emotion'].apply(lambda x: str(x)) + " in the text: " + train_df['source_text'] |
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train_df['stance'] = "Classify based on the features:" + train_df['target_stance'].apply(lambda x: str(x)) + " in the text: " + train_df['source_text'] |
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test_df['emotion_stance'] = "Classify based on the features:" + test_df['target_emotion_stance'].apply(lambda x: str(x)) + " in the text: " + test_df['source_text'] |
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test_df['emotion'] = "Classify based on the features:" + test_df['target_emotion'].apply(lambda x: str(x)) + " in the text: " + test_df['source_text'] |
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test_df['stance'] = "Classify based on the features:" + test_df['target_stance'].apply(lambda x: str(x)) + " in the text: " + test_df['source_text'] |
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val_df['emotion_stance'] = "Classify based on the features:" + val_df['target_emotion_stance'].apply(lambda x: str(x)) + " in the text: " + val_df['source_text'] |
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val_df['emotion'] = "Classify based on the features:" + val_df['target_emotion'].apply(lambda x: str(x)) + " in the text: " + val_df['source_text'] |
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val_df['stance'] = "Classify based on the features:" + val_df['target_stance'].apply(lambda x: str(x)) + " in the text: " + val_df['source_text'] |
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#label_dict = {0:'FAKE', 1:'REAL'} |
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train_df['target_text'] = train_df['target_text'].apply(lambda x: str(x)) |
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val_df['target_text'] = val_df['target_text'].apply(lambda x: str(x)) |
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test_df['target_text'] = test_df['target_text'].apply(lambda x: str(x)) |
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max_length = 500 |
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train_df = train_df[['source_text', 'target_text', #'src', #'dict_target_emotion_stance', |
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'target_emotion_stance', 'target_emotion', 'target_stance', |
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'emotion_stance', 'emotion', 'stance']] |
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test_df = test_df[['source_text', 'target_text', #'src', #'dict_target_emotion_stance', |
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'target_emotion_stance', 'target_emotion', 'target_stance', |
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'emotion_stance', 'emotion', 'stance']] |
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val_df = val_df[['source_text', 'target_text', #'src', #'dict_target_emotion_stance', |
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'target_emotion_stance', 'target_emotion', 'target_stance', |
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'emotion_stance', 'emotion', 'stance']] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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TrainingArguments( |
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output_dir="temp", |
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evaluation_strategy="epoch", |
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learning_rate=1e-3, |
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gradient_accumulation_steps=1, |
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#auto_find_batch_size=True, |
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num_train_epochs=2, |
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#save_steps=100, |
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weight_decay=0.01, |
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save_total_limit=2, |
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#optim="adafactor", |
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optim="adamw_torch_fused", |
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per_device_train_batch_size=128, |
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per_device_eval_batch_size=128, |
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# save_steps=1000, |
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# evaluation_strategy ="steps", |
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metric_for_best_model = 'eval_loss', #eval_loss |
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save_strategy="epoch", |
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load_best_model_at_end=True, |
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) |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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