AmazonScience/massive
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How to use cartesinus/multilingual_minilm-amazon_massive-intent_eu7 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="cartesinus/multilingual_minilm-amazon_massive-intent_eu7") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cartesinus/multilingual_minilm-amazon_massive-intent_eu7")
model = AutoModelForSequenceClassification.from_pretrained("cartesinus/multilingual_minilm-amazon_massive-intent_eu7")This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the MASSIVE 1.1 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.3523 | 1.0 | 5038 | 1.3058 | 0.6937 | 0.6937 |
| 0.7842 | 2.0 | 10076 | 0.8434 | 0.8059 | 0.8059 |
| 0.5359 | 3.0 | 15114 | 0.7231 | 0.8302 | 0.8302 |
| 0.4106 | 4.0 | 20152 | 0.7121 | 0.8443 | 0.8443 |
| 0.3294 | 5.0 | 25190 | 0.7366 | 0.8497 | 0.8497 |
| 0.2621 | 6.0 | 30228 | 0.7702 | 0.8528 | 0.8528 |
| 0.2164 | 7.0 | 35266 | 0.7773 | 0.8577 | 0.8577 |
| 0.1756 | 8.0 | 40304 | 0.8080 | 0.8569 | 0.8569 |
| 0.1625 | 9.0 | 45342 | 0.8162 | 0.8624 | 0.8624 |
| 0.1448 | 10.0 | 50380 | 0.8238 | 0.8623 | 0.8623 |
Base model
microsoft/Multilingual-MiniLM-L12-H384