readme: ---
  language: 
  - en
  thumbnail: 
  tags:
  - text-classification
  license: mit
  datasets:
  - imdb
  metrics:
  ---
  
  # IMDB Sentiment Task: roberta-base 
  
  ## Model description
  
  A simple base roBERTa model trained on the "imdb" dataset.
  
  ## Intended uses & limitations
  
  #### How to use
  
  ##### Transformers
  
  ```python
  # Load model and tokenizer
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForQuestionAnswering.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  
  # Use pipeline
  from transformers import pipeline
  
  model_name = "aychang/roberta-base-imdb"
  
  nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name)
  
  results = nlp(["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."])
  ```
  
  ##### AdaptNLP
  
  ```python
  from adaptnlp import EasySequenceClassifier
  
  model_name = "aychang/roberta-base-imdb"
  texts = ["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."]
  
  classifer = EasySequenceClassifier
  results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2)
  ```
  
  #### Limitations and bias
  
  This is minimal language model trained on a benchmark dataset.
  
  ## Training data
  
  IMDB https://huggingface.co/datasets/imdb
  
  ## Training procedure
  
  #### Hardware
  One V100
  
  #### Hyperparameters and Training Args
  ```python
  from transformers import TrainingArguments
  
  training_args = TrainingArguments(
      output_dir='./models',
      overwrite_output_dir=False,
      num_train_epochs=2,
      per_device_train_batch_size=8,
      per_device_eval_batch_size=8,
      warmup_steps=500,
      weight_decay=0.01,
      evaluation_strategy="steps",
      logging_dir='./logs',
      fp16=False,
      eval_steps=800,
      save_steps=300000
  )
  ```
  
  ## Eval results
  
  ```
  {'epoch': 2.0,
   'eval_accuracy': 0.94668,
   'eval_f1': array([0.94603457, 0.94731017]),
   'eval_loss': 0.2578844428062439,
   'eval_precision': array([0.95762642, 0.93624502]),
   'eval_recall': array([0.93472, 0.95864]),
   'eval_runtime': 244.7522,
   'eval_samples_per_second': 102.144}
  ```
  