End of training

README.md

@@ -0,0 +1,234 @@
+---
+language:
+- en
+license: cc-by-sa-4.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- DFKI-SLT/few-nerd
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: The Hebrew Union College libraries in Cincinnati and Los Angeles, the Library
+    of Congress in Washington, D.C ., the Jewish Theological Seminary in New York
+    City, and the Harvard University Library (which received donations of Deinard's
+    texts from Lucius Nathan Littauer, housed in Widener and Houghton libraries) also
+    have large collections of Deinard works.
+- text: Abu Abd Allah Muhammad al-Idrisi (1099–1165 or 1166), the Moroccan Muslim
+    geographer, cartographer, Egyptologist and traveller who lived in Sicily at the
+    court of King Roger II, mentioned this island, naming it جزيرة مليطمة ("jazīrat
+    Malīṭma", "the island of Malitma ") on page 583 of his book "Nuzhat al-mushtaq
+    fi ihtiraq ghal afaq", otherwise known as The Book of Roger, considered a geographic
+    encyclopaedia of the medieval world.
+- text: The font is also used in the logo of the American rock band Greta Van Fleet,
+    in the logo for Netflix show "Stranger Things ", and in the album art for rapper
+    Logic's album "Supermarket ".
+- text: Caretaker manager George Goss led them on a run in the FA Cup, defeating Liverpool
+    in round 4, to reach the semi-final at Stamford Bridge, where they were defeated
+    2–0 by Sheffield United on 28 March 1925.
+- text: In 1991, the National Science Foundation (NSF), which manages the U.S . Antarctic
+    Program (US AP), honoured his memory by dedicating a state-of-the-art laboratory
+    complex in his name, the Albert P. Crary Science and Engineering Center (CSEC)
+    located in McMurdo Station.
+pipeline_tag: token-classification
+base_model: bert-base-cased
+model-index:
+- name: SpanMarker with bert-base-cased on DFKI-SLT/few-nerd
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: DFKI-SLT/few-nerd
+      split: test
+    metrics:
+    - type: f1
+      value: 0.7705915921628306
+      name: F1
+    - type: precision
+      value: 0.7676710252037142
+      name: Precision
+    - type: recall
+      value: 0.7735344662974986
+      name: Recall
+---
+
+# SpanMarker with bert-base-cased on DFKI-SLT/few-nerd
+
+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 SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+- **Training Dataset:** [DFKI-SLT/few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
+- **Language:** en
+- **License:** cc-by-sa-4.0
+
+### Model Sources
+
+- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
+- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
+
+### Model Labels
+| Label        | Examples                                                                       |
+|:-------------|:-------------------------------------------------------------------------------|
+| art          | "The Seven Year Itch", "Imelda de ' Lambertazzi", "Time"                       |
+| building     | "Sheremetyevo International Airport", "Boston Garden", "Henry Ford Museum"     |
+| event        | "French Revolution", "Iranian Constitutional Revolution", "Russian Revolution" |
+| location     | "Croatian", "the Republic of Croatia", "Mediterranean Basin"                   |
+| organization | "Church 's Chicken", "Texas Chicken", "IAEA"                                   |
+| other        | "Amphiphysin", "BAR", "N-terminal lipid"                                       |
+| person       | "Hicks", "Edmund Payne", "Ellaline Terriss"                                    |
+| product      | "Corvettes - GT1 C6R", "Phantom", "100EX"                                      |
+
+## Evaluation
+
+### Metrics
+| Label        | Precision | Recall | F1     |
+|:-------------|:----------|:-------|:-------|
+| **all**      | 0.7677    | 0.7735 | 0.7706 |
+| art          | 0.7980    | 0.7349 | 0.7651 |
+| building     | 0.6420    | 0.6735 | 0.6574 |
+| event        | 0.6207    | 0.4977 | 0.5524 |
+| location     | 0.8137    | 0.8573 | 0.8350 |
+| organization | 0.7166    | 0.6809 | 0.6983 |
+| other        | 0.6707    | 0.6734 | 0.6721 |
+| person       | 0.8567    | 0.9144 | 0.8846 |
+| product      | 0.6786    | 0.6441 | 0.6609 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+# Run inference
+entities = model.predict("Caretaker manager George Goss led them on a run in the FA Cup, defeating Liverpool in round 4, to reach the semi-final at Stamford Bridge, where they were defeated 2–0 by Sheffield United on 28 March 1925.")
+```
+
+### Downstream Use
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+```python
+from span_marker import SpanMarkerModel, Trainer
+
+# 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
+
+# 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
+
+### Training Set Metrics
+| Training set          | Min | Median  | Max |
+|:----------------------|:----|:--------|:----|
+| Sentence length       | 1   | 24.4956 | 163 |
+| Entities per sentence | 0   | 2.5439  | 35  |
+
+### Training Hyperparameters
+- 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
+- mixed_precision_training: Native AMP
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.1629 | 200  | 0.0302          | 0.7137               | 0.6190            | 0.6630        | 0.9013              |
+| 0.3259 | 400  | 0.0237          | 0.7497               | 0.7108            | 0.7297        | 0.9257              |
+| 0.4888 | 600  | 0.0215          | 0.7622               | 0.7268            | 0.7441        | 0.9292              |
+| 0.6517 | 800  | 0.0213          | 0.7564               | 0.7619            | 0.7591        | 0.9355              |
+| 0.8147 | 1000 | 0.0196          | 0.7783               | 0.7648            | 0.7715        | 0.9384              |
+| 0.9776 | 1200 | 0.0196          | 0.7671               | 0.7783            | 0.7726        | 0.9390              |
+
+### Framework Versions
+- Python: 3.10.12
+- SpanMarker: 1.5.0
+- Transformers: 4.38.2
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## 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
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+{
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+  "<start>": 28996
+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
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+    "add_cross_attention": false,
+    "architectures": [
+      "BertForMaskedLM"
+    ],
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+    "forced_eos_token_id": null,
+    "gradient_checkpointing": false,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "O",
+      "1": "art",
+      "2": "building",
+      "3": "event",
+      "4": "location",
+      "5": "organization",
+      "6": "other",
+      "7": "person",
+      "8": "product"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
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+      "art": 1,
+      "building": 2,
+      "event": 3,
+      "location": 4,
+      "organization": 5,
+      "other": 6,
+      "person": 7,
+      "product": 8
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+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 512,
+    "min_length": 0,
+    "model_type": "bert",
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+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
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+    "position_embedding_type": "absolute",
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.38.2",
+    "type_vocab_size": 2,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 29000
+  },
+  "entity_max_length": 8,
+  "marker_max_length": 128,
+  "max_next_context": null,
+  "max_prev_context": null,
+  "model_max_length": 256,
+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.5.0",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.38.2",
+  "vocab_size": 29000
+}

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special_tokens_map.json

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tokenizer_config.json

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+}

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