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---
library_name: transformers
license: apache-2.0
datasets:
- stanfordnlp/imdb
language:
- en
metrics:
- accuracy
tags:
- minGRU
- sentiment_analysis
- custom_code
- hf_integration
---
# MinGRU Sentiment Analysis
First Hugging Face integration of minGRU models from the paper "[**Were RNNs All We Needed?**](https://arxiv.org/abs/2410.01201)".
This model uses BERT-Base-Uncased tokenizer and trained on default IMDB dataset.
Make sure you have installed "[**minGRU-pytorch**](https://github.com/lucidrains/minGRU-pytorch)" library by running "pip install minGRU-pytorch".
For modeling and configuration codes: [**minGRU-hf**](https://github.com/suayptalha/minGRU-hf/tree/main)
# Example Usage:
```py
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model = AutoModelForSequenceClassification.from_pretrained(
"suayptalha/minGRU-Sentiment-Analysis",
trust_remote_code = True
).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
text = "The movie was absolutely wonderful, I loved it!"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128).to("cuda")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
prediction = torch.argmax(logits, dim=-1).item()
sentiment = "positive" if prediction == 1 else "negative"
print(f"Text: {text}")
print(f"Predicted sentiment: {sentiment}")
```
> Text: The movie was absolutely wonderful, I loved it!
> Predicted sentiment: positive
# Training:
Training code:
```py
from torch.optim import AdamW
from torch.nn import CrossEntropyLoss
import matplotlib.pyplot as plt
from tqdm import tqdm
optimizer = AdamW(model.parameters(), lr=5e-5)
criterion = CrossEntropyLoss()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
num_epochs = 5
loss_values = []
for epoch in range(num_epochs):
model.train()
epoch_loss = 0
progress_bar = tqdm(train_dataloader, desc=f"Epoch {epoch + 1}")
for batch in progress_bar:
input_ids = batch["input_ids"].to(device)
labels = batch["label"].to(device)
optimizer.zero_grad()
outputs = model(input_ids=input_ids, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
epoch_loss += loss.item()
progress_bar.set_postfix(loss=epoch_loss / len(progress_bar))
avg_loss = epoch_loss / len(progress_bar)
loss_values.append(avg_loss)
# Loss Graph
plt.figure(figsize=(10, 6))
plt.plot(range(1, num_epochs + 1), loss_values, marker='o', label='Training Loss')
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Training Loss Over Epochs")
plt.legend()
plt.grid(True)
plt.show()
```
You can use this code snippet for fine-tuning!
# Loss Graph:
# Credits:
https://arxiv.org/abs/2410.01201
I am thankful to Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio and Hossein Hajimirsadeghi for their papers.
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