Instructions to use horsbug98/Part_2_mBERT_Model_E1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horsbug98/Part_2_mBERT_Model_E1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_2_mBERT_Model_E1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("horsbug98/Part_2_mBERT_Model_E1") model = AutoModelForQuestionAnswering.from_pretrained("horsbug98/Part_2_mBERT_Model_E1") - 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": 65.08951406649616,
|
| 4 |
+
"eval_f1": 78.76348644251458,
|
| 5 |
+
"eval_samples": 916,
|
| 6 |
+
"train_loss": 1.4328513269978806,
|
| 7 |
+
"train_runtime": 588.3802,
|
| 8 |
+
"train_samples": 14238,
|
| 9 |
+
"train_samples_per_second": 24.199,
|
| 10 |
+
"train_steps_per_second": 2.017
|
| 11 |
+
}
|