Model Card for Model ID
Simply put, this is a sentiment analyzer model. With the evolutions of ai models, a certain question hangs, will ai ever learn what the "SENTIMENT" is. Maybe yes, maybe no, we're uncertain, but this model brings us closer to the reality of AI understanding human sentiments.
Model Details
Model Description
This AI model tells a positive or negative sentiment on a phrase. This model is not a decoder but an encoder classification network model.
Inference Example : Review: 'The cinematography was absolutely breathtaking, but the plot fell completely flat.' Predicted Sentiment: Negative
Review: 'An absolute masterpiece. I haven't enjoyed a movie this much in years!' Predicted Sentiment: Positive
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: Havoc999
- Funded by [kaggle free tier]: [Thank you dear kaggle and gemini for helping me debug errors]
- Shared by [none other than me]
- Model type: [encoder classification network model]
- Language(s) (NLP): [English only]
- License: [The Unlicense]
You can get the onnx version at Havoc999/distilbert-imdb-sentiment-onnx
Training Details
Training Data
The IMDB dataset
Training Procedure
We load a pre-trained DistilBERT model with a sequence classification head configured for 2 classes (Positive / Negative).
Preprocessing [optional]
No preprocessing
Training Hyperparameters
training_args = TrainingArguments( output_dir="./results", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=2, weight_decay=0.01, eval_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, fp16=True, report_to="none" )
Environmental Impact
0.02 kg eq. CO2
Model Card Authors
Havoc999
Model Card Contact
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Model tree for Havoc999/distilbert-imdb-sentiment
Base model
distilbert/distilbert-base-uncased