Update Readme with details

README.md

@@ -6,6 +6,7 @@ tags:
 - ner
 - named-entity-recognition
 - generated_from_span_marker_trainer
+- muppet-roberta-large-ner
 datasets:
 - DFKI-SLT/few-nerd
 metrics:
@@ -14,27 +15,81 @@ metrics:
 - f1
 widget: []
 pipeline_tag: token-classification
+license: cc-by-sa-4.0
+language:
+- en
+model-index:
+  - name: >-
+      SpanMarker w. facebook/muppet-roberta-large on finegrained, supervised FewNERD by Radu-Sebastian Amarie
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          name: finegrained, supervised FewNERD
+          type: DFKI-SLT/few-nerd
+          config: supervised
+          split: test
+          revision: 6f0944f5a1d47c359b4f5de03ed1d58c98f297b5
+        metrics:
+          - type: f1
+            value: 0.705678
+            name: F1
+          - type: precision
+            value: 0.701648
+            name: Precision
+          - type: recall
+            value: 0.709755
+            name: Recall
+
 ---
 
 # SpanMarker
 
-This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [DFKI-SLT/few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition.
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [DFKI-SLT/few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition. 
+Training was done on a Nvidia 4090 in approximately 8 hours (but final chosen checkpoint was from before the first half of training)
+
+
+## Training and Validation Metrics
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630f2745982455e61cc5fb1d/TlEu3b2PmnptXc1pZ7C2u.png)
+
+Current model represents STEP 25000
+
+
+## Test Set Evaluation
+
+The following are some manually-selected checkpoints that correspond to the above steps:
+
+```
+|   checkpoint | Precision |   Recall   |      F1    |   Accuracy |   Runtime |   Samples/s | 
+|-------------:|----------:|-----------:|-----------:|-----------:|----------:|------------:|
+|        17000 |  0.706066 |   0.691239 |   0.698574 |   0.926213 |   335.172 |     123.474 | 
+|        18000 |  0.695331 |   0.700382 |   0.697847 |   0.926372 |   301.435 |     137.293 |
+|        19000 |  0.70618  |   0.693775 |   0.699923 |   0.926492 |   301.032 |     137.477 |
+|        20000 |  0.700665 |   0.701572 |   0.701118 |   0.927128 |   299.706 |     138.085 |
+|        21000 |  0.706467 |   0.695591 |   0.700987 |   0.926318 |   299.62  |     138.125 |
+|        22000 |  0.698079 |   0.710756 |   0.704361 |   0.928094 |   300.041 |     137.931 |
+|        24000 |  0.709286 |   0.695769 |   0.702463 |   0.926329 |   300.339 |     137.794 |
+|        25000 |  0.701648 |   0.709755 |   0.705678 |   0.92792  |   299.905 |     137.994 |
+|        26000 |  0.702509 |   0.708147 |   0.705317 |   0.927998 |   301.161 |     137.418 |
+|        27000 |  0.707315 |   0.698796 |   0.703029 |   0.926493 |   299.692 |     138.092 |
+```
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** SpanMarker
-<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
+**Encoder:** [muppet-roberta-large](https://huggingface.co/facebook/muppet-roberta-large)
 - **Maximum Sequence Length:** 256 tokens
 - **Maximum Entity Length:** 6 words
 - **Training Dataset:** [DFKI-SLT/few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Language:** en
+- **License:** cc-by-sa-4.0
 
-### Model Sources
+### Useful Links
 
-- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
-- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
+- Training was done with SpanMarker Trainer that can be found here: [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
 
 ## Uses
 
@@ -44,53 +99,25 @@ 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("eek/span-marker-muppet-roberta-large-fewnerd-fine-super")
 # Run inference
-entities = model.predict("None")
+entities = model.predict("His name was Radu.")
 ```
 
-### Downstream Use
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
+or it can be used directly in spacy via [SpanMarker](https://spacy.io/universe/project/span_marker).
 
 ```python
-from span_marker import SpanMarkerModel, Trainer
+import spacy
 
-# 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
+nlp = spacy.load("en_core_web_sm", exclude=["ner"])
+nlp.add_pipe("span_marker", config={"model": "tomaarsen/span-marker-roberta-large-ontonotes5"})
 
-# 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")
+text = """Cleopatra VII, also known as Cleopatra the Great, was the last active ruler of the \
+Ptolemaic Kingdom of Egypt. She was born in 69 BCE and ruled Egypt from 51 BCE until her \
+death in 30 BCE."""
+doc = nlp(text)
+print([(entity, entity.label_) for entity in doc.ents])
 ```
-</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
 
@@ -102,7 +129,32 @@ trainer.save_model("span_marker_model_id-finetuned")
 - Datasets: 2.18.0
 - Tokenizers: 0.15.2
 
-## Citation
+### Training Arguments
+
+```
+args = TrainingArguments(
+    output_dir="models/span-marker-muppet-roberta-large-fewnerd-fine-super",
+    learning_rate=1e-5,
+    gradient_accumulation_steps=2,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=8,
+    evaluation_strategy="steps",
+    save_strategy="steps",
+    save_steps=1000,
+    eval_steps=500,
+    push_to_hub=False,
+    logging_steps=50,
+    fp16=True,
+    warmup_ratio=0.1,
+    dataloader_num_workers=1,
+    load_best_model_at_end=True
+)
+```
+
+## Thanks
+
+Thanks to Tom Aarsen for the SpanMarker library.
 
 ### BibTeX
 ```
@@ -114,20 +166,6 @@ trainer.save_model("span_marker_model_id-finetuned")
 }
 ```
 
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
 ## Model Card Authors
 
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+- Done by [Radu-Sebastian Amarie](https://huggingface.co/eek)