MtCelesteMa/bert-base-uncased-finetuned-multiglue

Model Card for Model ID

This model is bert-base-uncased finetuned on the MultiGLUE dataset.

Model Details

Model Description

• Developed by: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): English
• License: Apache 2.0 (same as BERT)
• Finetuned from model [optional]: bert-base-uncased

Model Sources [optional]

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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

import numpy as np
import transformers

tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-uncased')
model = transformers.AutoModelForSequenceClassification.from_pretrained('MtCelesteMa/bert-base-uncased-finetuned-multiglue')

task = 'cola'
sentence1 = 'Our friends won\'t buy this analysis, let alone the next one we propose.'
sentence2 = None

inputs = tokenizer(f'{task}:{sentence1}', f'{sentence2}', return_tensors='pt')
outputs = model(**inputs)
label = np.argmax(outputs.logits[0].detach().numpy())
print(label)

Training Details

Training Data

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Training Procedure [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: RTX A6000
• Hours used: 2
• Cloud Provider: vast.ai
• Compute Region: Sweden
• Carbon Emitted: 0.26 kg

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Model Architecture and Objective

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