Text Classification
Transformers
Safetensors
English
xlm-roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tmnam20/xlm-roberta-base-vsfc-100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/xlm-roberta-base-vsfc-100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/xlm-roberta-base-vsfc-100")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/xlm-roberta-base-vsfc-100") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/xlm-roberta-base-vsfc-100") - Notebooks
- Google Colab
- Kaggle
Upload train_results.json with huggingface_hub
Browse files- train_results.json +8 -0
train_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 3.0,
|
| 3 |
+
"train_loss": 0.24354012940405023,
|
| 4 |
+
"train_runtime": 167.1261,
|
| 5 |
+
"train_samples": 11426,
|
| 6 |
+
"train_samples_per_second": 205.103,
|
| 7 |
+
"train_steps_per_second": 6.426
|
| 8 |
+
}
|