update model card README.md

README.md

@@ -1,36 +1,64 @@
-
 ---
-license: apache-2.0
-library_name: span-marker
 tags:
-- span-marker
-- token-classification
-- ner
-- named-entity-recognition
-pipeline_tag: token-classification
+- generated_from_trainer
+model-index:
+- name: span-marker-bert-base-multilingual-uncased-multinerd
+  results: []
 ---
 
-# SpanMarker for Named Entity Recognition
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# span-marker-bert-base-multilingual-uncased-multinerd
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0054
+- Overall Precision: 0.9275
+- Overall Recall: 0.9147
+- Overall F1: 0.9210
+- Overall Accuracy: 0.9842
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
 
-This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) as the underlying encoder. 
+## Training procedure
 
-## Usage
+### Training hyperparameters
 
-To use this model for inference, first install the `span_marker` library:
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
 
-```bash
-pip install span_marker
-```
+### Training results
 
-You can then run inference with this model like so:
+| Training Loss | Epoch | Step   | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0157        | 1.0   | 50369  | 0.0048          | 0.9143            | 0.8986         | 0.9064     | 0.9807           |
+| 0.003         | 2.0   | 100738 | 0.0047          | 0.9237            | 0.9126         | 0.9181     | 0.9835           |
+| 0.0017        | 3.0   | 151107 | 0.0054          | 0.9275            | 0.9147         | 0.9210     | 0.9842           |
 
-```python
-from span_marker import SpanMarkerModel
 
-# Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_name")
-# Run inference
-entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
-```
+### Framework versions
 
-See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.3
+- Tokenizers 0.13.3