flan-large-fibvib-pool

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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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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

[More Information Needed] 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,

)

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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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 presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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