universityofbucharest/laroseda
Updated • 231 • 1
How to use mateiaassAI/teacher_sst2_laroseda with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="mateiaassAI/teacher_sst2_laroseda") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mateiaassAI/teacher_sst2_laroseda")
model = AutoModelForSequenceClassification.from_pretrained("mateiaassAI/teacher_sst2_laroseda")This model is a fine-tuned version of mateiaassAI/teacher_sst2 on the laroseda 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 | F1 | Roc Auc | Accuracy | Precision | Recall |
|---|---|---|---|---|---|---|---|---|
| 0.1799 | 1.0 | 688 | 0.1426 | 0.9435 | 0.9434 | 0.943 | 0.9441 | 0.943 |
| 0.1071 | 2.0 | 1376 | 0.1906 | 0.9490 | 0.9490 | 0.949 | 0.9491 | 0.949 |
Base model
dumitrescustefan/bert-base-romanian-cased-v1