
# 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}
```

{"language": ["en"], "license": "mit", "tags": ["text-classification"], "datasets": ["imdb"]}
transformers pytorch jax roberta text-classification en dataset:imdb license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# IMDB Sentiment Task: roberta-base 

## Model description

A simple base roBERTa model trained on the "imdb" dataset.

## Intended uses & limitations

#### How to use

##### Transformers



##### AdaptNLP



#### Limitations and bias

This is minimal language model trained on a benchmark dataset.

## Training data

IMDB URL

## Training procedure

#### Hardware
One V100

#### Hyperparameters and Training Args


## Eval results



# IMDB Sentiment Task: roberta-base ## Model description

A simple base roBERTa model trained on the "imdb" dataset. ## Intended uses & limitations #### How to use ##### Transformers ##### AdaptNLP #### Limitations and bias

This is minimal language model trained on a benchmark dataset. ## Training data

IMDB URL ## Training procedure #### Hardware
One V100 #### Hyperparameters and Training Args ## Eval results
TAGS
#transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # IMDB Sentiment Task: roberta-base ## Model description

A simple base roBERTa model trained on the "imdb" dataset. ## Intended uses & limitations #### How to use ##### Transformers ##### AdaptNLP #### Limitations and bias

This is minimal language model trained on a benchmark dataset. ## Training data

IMDB URL ## Training procedure #### Hardware
One V100 #### Hyperparameters and Training Args ## Eval results
passage: TAGS
#transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# IMDB Sentiment Task: roberta-base## Model description

A simple base roBERTa model trained on the "imdb" dataset.## Intended uses & limitations#### How to use##### Transformers##### AdaptNLP#### Limitations and bias

This is minimal language model trained on a benchmark dataset.## Training data

IMDB URL## Training procedure#### Hardware
One V100#### Hyperparameters and Training Args## Eval results