Update README.md

README.md

@@ -101,53 +101,12 @@ This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained
 from span_marker import SpanMarkerModel
 
 # Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+model = SpanMarkerModel.from_pretrained("iahlt/span-marker-alephbert-small-nemo-mt-he")
 # Run inference
 entities = model.predict("יו\"ר ועדת ה נוער נתן סלובטיק אמר ש ה שחקנים של אנחנו לא משתלבים ב אירופה.")
+entities
 ```
 
-### Downstream Use
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-```python
-from span_marker import SpanMarkerModel, Trainer
-
-# Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_id")
-
-# Specify a Dataset with "tokens" and "ner_tag" columns
-dataset = load_dataset("conll2003") # For example CoNLL2003
-
-# Initialize a Trainer using the pretrained model & dataset
-trainer = Trainer(
-    model=model,
-    train_dataset=dataset["train"],
-    eval_dataset=dataset["validation"],
-)
-trainer.train()
-trainer.save_model("span_marker_model_id-finetuned")
-```
-</details>
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
 
 ## Training Details
 
@@ -170,36 +129,26 @@ trainer.save_model("span_marker_model_id-finetuned")
 - num_epochs: 4
 - mixed_precision_training: Native AMP
 
-### Training Results
-| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
-|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
-| 0.4070 | 1000 | 0.0352          | 0.0                  | 0.0               | 0.0           | 0.8980              |
-| 0.8140 | 2000 | 0.0327          | 0.0                  | 0.0               | 0.0           | 0.8980              |
-| 1.2210 | 3000 | 0.0224          | 0.0                  | 0.0               | 0.0           | 0.8980              |
-| 1.6280 | 4000 | 0.0149          | 0.5874               | 0.2200            | 0.3201        | 0.9134              |
-| 2.0350 | 5000 | 0.0137          | 0.55                 | 0.3895            | 0.4560        | 0.9248              |
-| 2.4420 | 6000 | 0.0113          | 0.6204               | 0.4313            | 0.5089        | 0.9298              |
-| 2.8490 | 7000 | 0.0121          | 0.5733               | 0.5075            | 0.5384        | 0.9310              |
-| 3.2560 | 8000 | 0.0115          | 0.5782               | 0.5236            | 0.5495        | 0.9334              |
-| 3.6630 | 9000 | 0.0108          | 0.6100               | 0.5354            | 0.5703        | 0.9359              |
-| 0.4070 | 1000 | 0.0103          | 0.6321               | 0.5880            | 0.6092        | 0.9381              |
-| 0.8140 | 2000 | 0.0088          | 0.6968               | 0.6288            | 0.6610        | 0.9471              |
-| 1.2210 | 3000 | 0.0091          | 0.6790               | 0.6695            | 0.6742        | 0.9484              |
-| 1.6280 | 4000 | 0.0086          | 0.6845               | 0.6845            | 0.6845        | 0.9480              |
-| 2.0350 | 5000 | 0.0089          | 0.6802               | 0.6845            | 0.6824        | 0.9492              |
-| 2.4420 | 6000 | 0.0084          | 0.6938               | 0.6953            | 0.6945        | 0.9539              |
-| 2.8490 | 7000 | 0.0088          | 0.6884               | 0.7039            | 0.6960        | 0.9512              |
-| 3.2560 | 8000 | 0.0086          | 0.6895               | 0.7124            | 0.7008        | 0.9514              |
-| 3.6630 | 9000 | 0.0082          | 0.6989               | 0.7049            | 0.7019        | 0.9526              |
-| 0.4070 | 1000 | 0.0080          | 0.7109               | 0.7124            | 0.7117        | 0.9535              |
-| 0.8140 | 2000 | 0.0074          | 0.7577               | 0.7114            | 0.7338        | 0.9567              |
-| 1.2210 | 3000 | 0.0083          | 0.7183               | 0.7414            | 0.7297        | 0.9554              |
-| 1.6280 | 4000 | 0.0088          | 0.6987               | 0.7339            | 0.7159        | 0.9510              |
-| 2.0350 | 5000 | 0.0086          | 0.7135               | 0.7296            | 0.7215        | 0.9541              |
-| 2.4420 | 6000 | 0.0086          | 0.7167               | 0.7382            | 0.7273        | 0.9559              |
-| 2.8490 | 7000 | 0.0088          | 0.7133               | 0.7554            | 0.7337        | 0.9541              |
-| 3.2560 | 8000 | 0.0085          | 0.7165               | 0.7511            | 0.7334        | 0.9551              |
-| 3.6630 | 9000 | 0.0083          | 0.7263               | 0.7489            | 0.7375        | 0.9561              |
+
+### Evaluation results
+
+|                        |          0 |
+|:-----------------------|-----------:|
+| eval_loss              | 0.00487611 |
+| eval_overall_precision | 0.822917   |
+| eval_overall_recall    | 0.791583   |
+| eval_overall_f1        | 0.806946   |
+| eval_overall_accuracy  | 0.969029   |
+
+### Test results
+
+|                        |          0 |
+|:-----------------------|-----------:|
+| test_loss              | 0.00652107 |
+| test_overall_precision | 0.747289   |
+| test_overall_recall    | 0.73927    |
+| test_overall_f1        | 0.743258   |
+| test_overall_accuracy  | 0.960126   |
 
 ### Framework Versions
 - Python: 3.10.12