Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dsmsb/16class_800_newtest_xlm_roberta_base_25nov_v1_5epoch")
model = AutoModelForSequenceClassification.from_pretrained("dsmsb/16class_800_newtest_xlm_roberta_base_25nov_v1_5epoch")Quick Links
16class_800_newtest_xlm_roberta_base_25nov_v1_5epoch
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2282
- Accuracy: 0.9359
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: 1e-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 | Accuracy |
|---|---|---|---|---|
| 1.7509 | 1.0 | 765 | 0.6382 | 0.8137 |
| 0.6154 | 2.0 | 1530 | 0.4188 | 0.8781 |
| 0.4852 | 3.0 | 2295 | 0.3183 | 0.9094 |
| 0.3598 | 4.0 | 3060 | 0.2492 | 0.9290 |
| 0.3174 | 5.0 | 3825 | 0.2282 | 0.9359 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for dsmsb/16class_800_newtest_xlm_roberta_base_25nov_v1_5epoch
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dsmsb/16class_800_newtest_xlm_roberta_base_25nov_v1_5epoch")