update model card README.md

README.md

@@ -1,36 +1,75 @@
-
 ---
-license: apache-2.0
-library_name: span-marker
 tags:
-- span-marker
-- token-classification
-- ner
-- named-entity-recognition
-pipeline_tag: token-classification
+- generated_from_trainer
+datasets:
+- few-nerd
+model-index:
+- name: span-marker-robert-base
+  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-robert-base
+
+This model is a fine-tuned version of [](https://huggingface.co/) on the few-nerd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0214
+- Overall Precision: 0.7642
+- Overall Recall: 0.7947
+- Overall F1: 0.7791
+- Overall Accuracy: 0.9397
+
+## 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 [roberta-base](https://huggingface.co/roberta-base) 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: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
 
-```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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0214        | 0.08  | 100  | 0.0219          | 0.7641            | 0.7679         | 0.7660     | 0.9330           |
+| 0.0199        | 0.16  | 200  | 0.0243          | 0.7442            | 0.7679         | 0.7559     | 0.9348           |
+| 0.0179        | 0.24  | 300  | 0.0212          | 0.7730            | 0.7580         | 0.7654     | 0.9361           |
+| 0.0188        | 0.33  | 400  | 0.0225          | 0.7616            | 0.7710         | 0.7662     | 0.9343           |
+| 0.0149        | 0.41  | 500  | 0.0240          | 0.7537            | 0.7783         | 0.7658     | 0.9375           |
+| 0.015         | 0.49  | 600  | 0.0230          | 0.7540            | 0.7829         | 0.7682     | 0.9362           |
+| 0.0137        | 0.57  | 700  | 0.0232          | 0.7746            | 0.7538         | 0.7640     | 0.9319           |
+| 0.0123        | 0.65  | 800  | 0.0218          | 0.7651            | 0.7879         | 0.7763     | 0.9393           |
+| 0.0103        | 0.73  | 900  | 0.0223          | 0.7688            | 0.7964         | 0.7824     | 0.9397           |
+| 0.0108        | 0.82  | 1000 | 0.0209          | 0.7763            | 0.7816         | 0.7789     | 0.9397           |
+| 0.0116        | 0.9   | 1100 | 0.0213          | 0.7743            | 0.7879         | 0.7811     | 0.9398           |
+| 0.0119        | 0.98  | 1200 | 0.0214          | 0.7653            | 0.7947         | 0.7797     | 0.9400           |
 
-```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+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3