Instructions to use horsbug98/Part_1_XLM_Model_E1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horsbug98/Part_1_XLM_Model_E1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_1_XLM_Model_E1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("horsbug98/Part_1_XLM_Model_E1") model = AutoModelForQuestionAnswering.from_pretrained("horsbug98/Part_1_XLM_Model_E1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload all_results.json
Browse files- all_results.json +11 -0
all_results.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 1.0,
|
| 3 |
+
"eval_exact_match": 68.41432225063939,
|
| 4 |
+
"eval_f1": 81.51977337474554,
|
| 5 |
+
"eval_samples": 842,
|
| 6 |
+
"train_loss": 1.6402719195893076,
|
| 7 |
+
"train_runtime": 2258.7241,
|
| 8 |
+
"train_samples": 53764,
|
| 9 |
+
"train_samples_per_second": 23.803,
|
| 10 |
+
"train_steps_per_second": 1.984
|
| 11 |
+
}
|