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README.md

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+---
+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:
+- tomaarsen/ner-orgs
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: 'The entourage was the largest ever to accompany an ROC president abroad,
+    and included: Chuang Ming - yao -LRB- secretary - general, National Security Council
+    -RRB-, Tien Hung - mao -LRB- minister of foreign affairs -RRB-, Lin Hsin - yi
+    -LRB- minister of economic affairs -RRB-, Chen Po - chih -LRB- chairman, Council
+    of Economic Planning and Development -RRB-, Chen Hsi - huang -LRB- chairman, Council
+    of Agriculture -RRB-, Chung Chin -LRB- head of the Government Information Office
+    -RRB-, Jeffrey Koo -LRB- chairman of the National Association of Industry and
+    Commerce -RRB-, Wang Yu - tseng -LRB- chairman of the General Chamber of Commerce
+    of the ROC -RRB-, and Lin Kun - chung -LRB- chairman of the Chinese National Federation
+    of Industries -RRB-.'
+- text: During the period, IPC monopolized oil exploration inside the Red Line; excluding
+    Saudi Arabia and Bahrain, where ARAMCO (formed in 1944 by renaming of the Saudi
+    subsidiary of Standard Oil of California (Socal)) and Bahrain Petroleum Company
+    (BAPCO) respectively held controlling position.
+- text: In the early decades of the 20th century, Benoytosh Bhattacharya – an expert
+    on Tantra and the then director of the Oriental Institute of Baroda – studied
+    various texts such as the Buddhist "Sadhanamala "(1156CE), the Hindu "Chhinnamastakalpa
+    "(uncertain date), and the "Tantrasara "by Krishnananda Agamavagisha (late 16th
+    century).
+- text: A united opposition of fourteen political parties organized into the National
+    Opposition Union (Unión Nacional Oppositora, UNO) with the support of the United
+    States National Endowment for Democracy.
+- text: Lockheed said the U.S. Navy may also buy an additional 340 trainer aircraft
+    to replace its T34C trainers made by the Beech Aircraft Corp. unit of Raytheon
+    Corp.
+pipeline_tag: token-classification
+co2_eq_emissions:
+  emissions: 67.50149039261815
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.629
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+base_model: prajjwal1/bert-small
+model-index:
+- name: SpanMarker with prajjwal1/bert-small on FewNERD, CoNLL2003, and OntoNotes
+    v5
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: FewNERD, CoNLL2003, and OntoNotes v5
+      type: tomaarsen/ner-orgs
+      split: test
+    metrics:
+    - type: f1
+      value: 0.7438057260629957
+      name: F1
+    - type: precision
+      value: 0.7474561008554705
+      name: Precision
+    - type: recall
+      value: 0.7401908328874621
+      name: Recall
+---
+
+# SpanMarker with prajjwal1/bert-small on FewNERD, CoNLL2003, and OntoNotes v5
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+- **Training Dataset:** [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs)
+- **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                                     |
+|:------|:---------------------------------------------|
+| ORG   | "Texas Chicken", "Church 's Chicken", "IAEA" |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.7475    | 0.7402 | 0.7438 |
+| ORG     | 0.7475    | 0.7402 | 0.7438 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-small-orgs")
+# Run inference
+entities = model.predict("Lockheed said the U.S. Navy may also buy an additional 340 trainer aircraft to replace its T34C trainers made by the Beech Aircraft Corp. unit of Raytheon Corp.")
+```
+
+### 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("tomaarsen/span-marker-bert-small-orgs")
+
+# 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("tomaarsen/span-marker-bert-small-orgs-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   | 23.5706 | 263 |
+| Entities per sentence | 0   | 0.7865  | 39  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 128
+- eval_batch_size: 128
+- 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: 3
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.5720 | 600  | 0.0085          | 0.7230               | 0.6552            | 0.6874        | 0.9641              |
+| 1.1439 | 1200 | 0.0078          | 0.7324               | 0.7021            | 0.7169        | 0.9663              |
+| 1.7159 | 1800 | 0.0074          | 0.7499               | 0.7213            | 0.7353        | 0.9679              |
+| 2.2879 | 2400 | 0.0074          | 0.7611               | 0.7318            | 0.7462        | 0.9701              |
+| 2.8599 | 3000 | 0.0072          | 0.772                | 0.7268            | 0.7487        | 0.9700              |
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.068 kg of CO2
+- **Hours Used**: 0.629 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.9.16
+- SpanMarker: 1.5.1.dev
+- Transformers: 4.30.0
+- PyTorch: 2.0.1+cu118
+- Datasets: 2.14.0
+- Tokenizers: 0.13.3
+
+## 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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+{
+  "<end>": 30523,
+  "<start>": 30522
+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "prajjwal1/bert-small",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_probs_dropout_prob": 0.1,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "classifier_dropout": null,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 512,
+    "id2label": {
+      "0": "O",
+      "1": "B-ORG",
+      "2": "I-ORG"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 2048,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "B-ORG": 1,
+      "I-ORG": 2,
+      "O": 0
+    },
+    "layer_norm_eps": 1e-12,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 512,
+    "min_length": 0,
+    "model_type": "bert",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 8,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 4,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 0,
+    "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": "4.30.0",
+    "type_vocab_size": 2,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 30524
+  },
+  "entity_max_length": 8,
+  "id2label": {
+    "0": "O",
+    "1": "ORG"
+  },
+  "id2reduced_id": {
+    "0": 0,
+    "1": 1,
+    "2": 1
+  },
+  "label2id": {
+    "O": 0,
+    "ORG": 1
+  },
+  "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": "1.5.1.dev",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.30.0",
+  "vocab_size": 30524
+}

pytorch_model.bin

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+oid sha256:0b2746c1382c0ffeeeefb0de829f38c75ba3ddf9e5de8ad14277e0c443470c2e
+size 115096015

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "entity_max_length": 8,
+  "marker_max_length": 128,
+  "mask_token": "[MASK]",
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}