End of training

README.md

@@ -1,75 +1,36 @@
+
 ---
+license: apache-2.0
+library_name: span-marker
 tags:
-- generated_from_trainer
-datasets:
-- few-nerd
-model-index:
-- name: span-marker-robert-base
-  results: []
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+pipeline_tag: token-classification
 ---
 
-<!-- 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
+# SpanMarker for Named Entity Recognition
 
-## Training procedure
+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 hyperparameters
+## Usage
 
-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
+To use this model for inference, first install the `span_marker` library:
 
-### Training results
+```bash
+pip install span_marker
+```
 
-| 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           |
+You can then run inference with this model like so:
 
+```python
+from span_marker import SpanMarkerModel
 
-### Framework versions
+# 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.")
+```
 
-- Transformers 4.30.2
-- Pytorch 2.0.1+cu118
-- Datasets 2.13.1
-- Tokenizers 0.13.3
+See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.