minGRU-Sentiment-Analysis

12

4

license:apache-2.0

by

suayptalha

Other

OTHER

New

12 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

Unknown

Mobile

Laptop

Server

Quick Summary

First Hugging Face integration of minGRU models from the paper "Were RNNs All We Needed?

Code Examples

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Example Usage:pythontransformers

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}")

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.