Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Zlovoblachko/L1-classifier-WFT")
model = AutoModelForSequenceClassification.from_pretrained("Zlovoblachko/L1-classifier-WFT")Quick Links
L1-classifier-WFT
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3079
- F1: 0.9119
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 83 | 0.5851 | 0.6476 |
| No log | 2.0 | 166 | 0.5009 | 0.7726 |
| No log | 3.0 | 249 | 0.3615 | 0.8504 |
| No log | 4.0 | 332 | 0.3121 | 0.9119 |
| No log | 5.0 | 415 | 0.3079 | 0.9119 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for Zlovoblachko/L1-classifier-WFT
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zlovoblachko/L1-classifier-WFT")