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]
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- Model type: [More Information Needed]
- Language(s) (NLP): English
- License: Apache 2.0 (same as BERT)
- Finetuned from model [optional]: bert-base-uncased
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Uses
Direct 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.
1import numpy as np
2import transformers
3
4tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-uncased')
5model = transformers.AutoModelForSequenceClassification.from_pretrained('MtCelesteMa/bert-base-uncased-finetuned-multiglue')
6
7task = 'cola'
8sentence1 = 'Our friends won\'t buy this analysis, let alone the next one we propose.'
9sentence2 = None
10
11inputs = tokenizer(f'{task}:{sentence1}', f'{sentence2}', return_tensors='pt')
12outputs = model(**inputs)
13label = np.argmax(outputs.logits[0].detach().numpy())
14print(label)
Training Details
Training Data
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Evaluation
Testing Data, Factors & Metrics
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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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