Classifier Prompted
Collection
Classifier retrained with prompt used during project • 3 items • Updated
How to use PracticalWork/xlm-roberta-large-classifier-prompted with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="PracticalWork/xlm-roberta-large-classifier-prompted") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("PracticalWork/xlm-roberta-large-classifier-prompted")
model = AutoModelForSequenceClassification.from_pretrained("PracticalWork/xlm-roberta-large-classifier-prompted")This model is a fine-tuned version of PracticalWork/xlm-roberta-large-classifier on an unknown 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 |
|---|---|---|---|---|---|
| No log | 0 | 0 | 0.8239 | 0.7029 | 0.6341 |
| No log | 0.6 | 255 | 0.3202 | 0.8832 | 0.8681 |
| 0.3462 | 1.2 | 510 | 0.2943 | 0.8827 | 0.8824 |
| 0.3462 | 1.8 | 765 | 0.2771 | 0.8967 | 0.8942 |
| 0.2258 | 2.4 | 1020 | 0.3878 | 0.8768 | 0.8796 |
| 0.2258 | 3.0 | 1275 | 0.3406 | 0.8891 | 0.8898 |
Base model
FacebookAI/xlm-roberta-large