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README.md

@@ -1,126 +1,133 @@
-
 ---
-license: apache-2.0
 library_name: span-marker
 tags:
 - span-marker
 - token-classification
 - ner
 - named-entity-recognition
-pipeline_tag: token-classification
-widget:
-- text: "here, da = direct assessment, rr = relative ranking, ds = discrete scale and cs = continuous scale."
-  example_title: "Uncased 1"
-- text: "modifying or replacing the erasable programmable read only memory (eprom) in a phone would allow the configuration of any esn and min via software for cellular devices."
-  example_title: "Uncased 2"
-- text: "we propose a technique called aggressive stochastic weight averaging (aswa) and an extension called norm-filtered aggressive stochastic weight averaging (naswa) which improves te stability of models over random seeds."
-  example_title: "Uncased 3"
-- text: "the choice of the encoder and decoder modules of dnpg can be quite flexible, for instance long-short term memory networks (lstm) or convolutional neural network (cnn)."
-  example_title: "Uncased 4"
-model-index:
-  - name: SpanMarker w. bert-base-uncased on Acronym Identification by Tom Aarsen
-    results:
-      - task:
-          type: token-classification
-          name: Named Entity Recognition
-        dataset:
-          type: acronym_identification
-          name: Acronym Identification
-          split: validation
-          revision: c3c245a18bbd57b1682b099e14460eebf154cbdf
-        metrics:
-          - type: f1
-            value: 0.9198
-            name: F1
-          - type: precision
-            value: 0.9252
-            name: Precision
-          - type: recall
-            value: 0.9145
-            name: Recall
-datasets:
-  - acronym_identification
-language:
-  - en
+- generated_from_span_marker_trainer
 metrics:
-  - f1
-  - recall
-  - precision
+- precision
+- recall
+- f1
+widget: []
+pipeline_tag: token-classification
 ---
 
-# SpanMarker for uncased Acronyms Named Entity Recognition
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition.
 
-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-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.
+## Model Details
 
-Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: 
-[tomaarsen/span-marker-bert-base-acronyms](https://huggingface.co/tomaarsen/span-marker-bert-base-acronyms).
+### Model Description
 
-## Metrics
+- **Model Type:** SpanMarker
+<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
-It achieves the following results on the validation set:
-- Overall Precision: 0.9252
-- Overall Recall: 0.9145
-- Overall F1: 0.9198
-- Overall Accuracy: 0.9797
+### Model Sources
 
-## Labels
+- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
+- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
 
-| **Label** | **Examples** |
-|-----------|--------------|
-| SHORT     | "nlp", "coqa", "soda", "sca" | 
-| LONG      | "natural language processing", "conversational question answering", "symposium on discrete algorithms", "successive convex approximation" |
+## Uses
 
-## Usage
+### Direct Use for Inference
 
-To use this model for inference, first install the `span_marker` library:
+```python
+from span_marker import SpanMarkerModel
 
-```bash
-pip install span_marker
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+# Run inference
+entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
 ```
 
-You can then run inference with this model like so:
+### Downstream Use
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
 
 ```python
-from span_marker import SpanMarkerModel
+from span_marker import SpanMarkerModel, Trainer
 
 # Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-acronyms")
-# Run inference
-entities = model.predict("compression algorithms like principal component analysis (pca) can reduce noise and complexity.")
+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
+
+### Framework Versions
+
+- Python: 3.9.16
+- SpanMarker: 1.3.1.dev
+- Transformers: 4.30.0
+- PyTorch: 2.0.1+cu118
+- Datasets: 2.14.0
+- Tokenizers: 0.13.2
+
+## Citation
+
+### BibTeX
+```
+@software{Aarsen_SpanMarker,
+    author = {Aarsen, Tom},
+    license = {Apache-2.0},
+    title = {{SpanMarker for Named Entity Recognition}},
+    url = {https://github.com/tomaarsen/SpanMarkerNER}
+}
+```
+
+<!--
+## 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
 
-See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 32
-- eval_batch_size: 32
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 2
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.013         | 0.31  | 200  | 0.0101          | 0.8998            | 0.8514         | 0.8749     | 0.9696           |
-| 0.0088        | 0.62  | 400  | 0.0082          | 0.8997            | 0.9142         | 0.9069     | 0.9764           |
-| 0.0082        | 0.94  | 600  | 0.0071          | 0.9173            | 0.8955         | 0.9063     | 0.9765           |
-| 0.0063        | 1.25  | 800  | 0.0066          | 0.9210            | 0.9187         | 0.9198     | 0.9802           |
-| 0.0066        | 1.56  | 1000 | 0.0066          | 0.9302            | 0.8941         | 0.9118     | 0.9783           |
-| 0.0064        | 1.87  | 1200 | 0.0063          | 0.9304            | 0.9042         | 0.9171     | 0.9792           |
-| 0.0063        | 2.00  | 1290 | 0.0063          | 0.9252            | 0.9145         | 0.9198     | 0.9797           |
-
-### Framework versions
-
-- SpanMarker 1.2.4
-- Transformers 4.31.0
-- Pytorch 1.13.1+cu117
-- Datasets 2.14.3
-- Tokenizers 0.13.2
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "models\\span_marker_bert_base_uncased_acronyms\\checkpoint-final",
+  "_name_or_path": "models\\tomaarsen\\span-marker-bert-base-uncased-acronyms-2\\checkpoint-final",
   "architectures": [
     "SpanMarkerModel"
   ],
@@ -84,7 +84,7 @@
     "top_p": 1.0,
     "torch_dtype": null,
     "torchscript": false,
-    "transformers_version": "4.31.0",
+    "transformers_version": "4.30.0",
     "type_vocab_size": 2,
     "typical_p": 1.0,
     "use_bfloat16": false,
@@ -94,8 +94,8 @@
   "entity_max_length": 8,
   "id2label": {
     "0": "O",
-    "1": "LONG",
-    "2": "SHORT"
+    "1": "long",
+    "2": "short"
   },
   "id2reduced_id": {
     "0": 1,
@@ -106,8 +106,8 @@
   },
   "label2id": {
     "O": 0,
-    "LONG": 1,
-    "SHORT": 2
+    "long": 1,
+    "short": 2
   },
   "marker_max_length": 128,
   "max_next_context": null,
@@ -115,9 +115,9 @@
   "model_max_length": 256,
   "model_max_length_default": 512,
   "model_type": "span-marker",
-  "span_marker_version": "1.2.5.dev",
+  "span_marker_version": "1.3.1.dev",
   "torch_dtype": "float32",
   "trained_with_document_context": false,
-  "transformers_version": "4.31.0",
+  "transformers_version": "4.30.0",
   "vocab_size": 30524
 }

pytorch_model.bin

@@ -1,3 +1,3 @@
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+size 438024117