---
library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

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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.

- **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]

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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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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']
train_df['emotion'] = "Classify based on the features:" + train_df['target_emotion'].apply(lambda x: str(x)) + " in the text: "  + train_df['source_text']
train_df['stance'] = "Classify based on the features:" + train_df['target_stance'].apply(lambda x: str(x)) + " in the text: "  + train_df['source_text']

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']
test_df['emotion'] = "Classify based on the features:" + test_df['target_emotion'].apply(lambda x: str(x)) + " in the text: "  + test_df['source_text']
test_df['stance'] = "Classify based on the features:" + test_df['target_stance'].apply(lambda x: str(x)) + " in the text: "  + test_df['source_text']

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']
val_df['emotion'] = "Classify based on the features:" + val_df['target_emotion'].apply(lambda x: str(x)) + " in the text: "  + val_df['source_text']
val_df['stance'] = "Classify based on the features:" + val_df['target_stance'].apply(lambda x: str(x)) + " in the text: "  + val_df['source_text']

#label_dict = {0:'FAKE', 1:'REAL'}

train_df['target_text'] = train_df['target_text'].apply(lambda x: str(x))
val_df['target_text'] = val_df['target_text'].apply(lambda x: str(x))
test_df['target_text'] = test_df['target_text'].apply(lambda x: str(x))

max_length = 500

train_df = train_df[['source_text', 'target_text', #'src', #'dict_target_emotion_stance',
                      'target_emotion_stance', 'target_emotion', 'target_stance',
                      'emotion_stance', 'emotion', 'stance']]

test_df = test_df[['source_text', 'target_text', #'src', #'dict_target_emotion_stance',
                      'target_emotion_stance', 'target_emotion', 'target_stance',
                      'emotion_stance', 'emotion', 'stance']]

val_df = val_df[['source_text', 'target_text', #'src', #'dict_target_emotion_stance',
                      'target_emotion_stance', 'target_emotion', 'target_stance',
                      'emotion_stance', 'emotion', 'stance']]


#### 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 -->

TrainingArguments(
    output_dir="temp",
    evaluation_strategy="epoch",
    learning_rate=1e-3,
    gradient_accumulation_steps=1,
    #auto_find_batch_size=True,
    num_train_epochs=2,
    #save_steps=100,
    weight_decay=0.01,
    save_total_limit=2,


    #optim="adafactor",
    optim="adamw_torch_fused",
    per_device_train_batch_size=128,
    per_device_eval_batch_size=128,

    # save_steps=1000,
    # evaluation_strategy ="steps",
    metric_for_best_model = 'eval_loss',  #eval_loss
    save_strategy="epoch",
    load_best_model_at_end=True,
)

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

- **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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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