README.md

---
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
- legal
metrics:
- precision
- recall
- f1
widget:
- text: >-
    The seven-judge Constitution Bench of the Supreme Court in SBP and Co.
    (supra) while reversing earlier five-judge Constitution Bench judgment in
    Konkan Railway Corpn. Ltd. vs. Rani Construction (P) Ltd., (2002) 2 SCC 388
    held that the power exercised by the Chief Justice of the High Court or the
    Chief justice of India under Section 11(6) of the Arbitration Act is not an
    administrative power but is a judicial power.
- text: >-
    In The High Court Of Judicature At Patna Criminal Writ Jurisdiction Case
    No.160 of 2021 Arising Out of Ps. Case No.-58 Year-2020 Thana- Bakhari
    District- Begusarai ======================================================
    Hanif Ur Rahman, son of Azhar Rahman, Resident of C-39, East Nizamuddin, New
    Delhi....... Petitioner Versus 1. The State of Bihar (through Chief
    Secretary, Govt. of Bihar) Main Secretariat, Patna - 800015. 2. Meena
    Khatoon, wife of Mastan @ Noor Mohammad, Resident of Village- Mansurpur
    Chaksikandar, P.S.- Bidupur, District- Vaishali (Bihar) 3. The Bihar Police,
    through Standing Counsel. 4. Child Welfare Committee, through Chairperson,
    Chanakyanagar, Mahmadpur, Begusarai. 5. The Superintendent, Alpawas Grih,
    Nirala Nagar, Behind G.D. College, Ratanpur, Begusarai....... Respondents
    ====================================================== Appearance:For the
    Petitioner:Ms. Kriti Awasthi, Advocate Mr. Sambhav Gupta, Advocate Mr.
    Navnit Kumar, Advocate Mr. Shyam Kumar, Advocate For the
    Respondents:Mr.Nadim Seraj, G.P.5 For the Resp. No. 2:Ms. Archana Sinha,
    Advocate For the Resp. No. 4:Mr. Prabhu Narain Sharma, Advocate
    ====================================================== Coram: Honourable Mr.
    Justice Rajeev Ranjan Prasad C.A.V. Judgment
- text: >-
    1 R In The High Court Of Karnataka At Bengaluru Dated This The 19Th Day Of
    February, 2021 Before The Hon'Ble Mr. Justice H.P. Sandesh Criminal Appeal
    No.176/2011 Between: Sri G.L. Jagadish, S/O Sri G.N. Lingappa, Aged About 52
    Years, Residing At No.29, 3Rd Main, Basaveshwara Housing Society Layout,
    Vijayanagar, Near Bts Depot, Bengaluru-40....Appellant [By Sri H.
    Ramachandra, Advocate For Sri H.R. Anantha Krishna Murthy And Associates -
    (Through V.C.)] And: Smt. Vasantha Kokila, W/O Late N.R. Somashekhar, Aged
    About 58 Years, Residing At No.322, 8Th Main, 3Rd Stage, 4Th Block,
    Basaveshwaranagar, Bengaluru....Respondent [By Sri K.R. Lakshminarayana Rao,
    Advocate] This Criminal Appeal Is Filed Under Section 378(4) Of Cr.P.C.
    Praying To Set Aside The Order Dated 06.07.2010 Passed By The P.O. Ftc-Ii,
    Bengaluru In Crl.A. No.470/2009 And Confirming The Order Dated 27.05.2009
    Passed By The Xxii Acmm And Xxiv Ascj, Bengaluru In C.C.No.17229/2004
    Convicting The Respondent/Accused For The Offence Punishable Under Section
    138 Of Ni Act. 2 This Criminal Appeal Having Been Heard And Reserved For
    Orders On 06.02.2021 This Day, The Court Pronounced The Following: Judgment
- text: >-
    The petition was filed through Sh. Vijay Pahwa, General Power of Attorney
    and it was asserted in the petition under Section 13-B of the Rent Act that
    1 of 23 50% share of the demised premises had been purchased by the landlord
    from Sh. Vinod Malhotra vide sale deed No.4226 registered on 20.12.2007 with
    Sub Registrar, Chandigarh.
- text: >-
    Mr. Arun Bharadwaj, ld. CGSC, appearing for the Union of India, has
    Signature Not Verified Digitally Signed By:PRATHIBA M SINGH Signing
    Date:09.10.2020 16:15 Digitally Signed By:SINDHU KRISHNAKUMAR Signing
    Date:09.10.2020 16:50:02 reiterated the submissions made by Dr. Singhvi and
    has further submitted that this petition ought to be heard with the OA No.
    291/138/2020 pending before the CAT.
pipeline_tag: token-classification
model-index:
- name: SpanMarker
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: legal_ner
      type: unknown
      split: eval
    metrics:
    - type: f1
      value: 0.9099756690997567
      name: F1
    - type: precision
      value: 0.9089703932832524
      name: Precision
    - type: recall
      value: 0.9109831709477414
      name: Recall
---

# SpanMarker

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. It was trained on the Legal NER Indian Justice dataset.

## Model Details

### Model Description
- **Model Type:** SpanMarker
<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 128 tokens
- **Maximum Entity Length:** 6 words
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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)

                                                                                                 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel
from span_marker.tokenizer import SpanMarkerTokenizer


# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-legal")
tokenizer = SpanMarkerTokenizer.from_pretrained("roberta-base", config=model.config)
model.set_tokenizer(tokenizer)

# Run inference
entities = model.predict("The petition was filed through Sh. Vijay Pahwa, General Power of Attorney and it was asserted in the petition under Section 13-B of the Rent Act that 1 of 23 50% share of the demised premises had been purchased by the landlord from Sh. Vinod Malhotra vide sale deed No.4226 registered on 20.12.2007 with Sub Registrar, Chandigarh.")
```

### Downstream Use
You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

```python
from span_marker import SpanMarkerModel, Trainer
from span_marker.tokenizer import SpanMarkerTokenizer


# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-legal")
tokenizer = SpanMarkerTokenizer.from_pretrained("roberta-base", config=model.config)
model.set_tokenizer(tokenizer)

# 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("lambdavi/span-marker-luke-legal-finetuned")
```
</details>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max  |
|:----------------------|:----|:--------|:-----|
| Sentence length       | 3   | 44.5113 | 2795 |
| Entities per sentence | 0   | 2.7232  | 68   |

### Training Hyperparameters
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5

### Training Results
| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.9997 | 1837 | 0.0137          | 0.7773               | 0.7994            | 0.7882        | 0.9577              |
| 2.0    | 3675 | 0.0090          | 0.8751               | 0.8348            | 0.8545        | 0.9697              |
| 2.9997 | 5512 | 0.0077          | 0.8777               | 0.8959            | 0.8867        | 0.9770              |
| 4.0    | 7350 | 0.0061          | 0.8941               | 0.9083            | 0.9011        | 0.9811              |
| 4.9986 | 9185 | 0.0064          | 0.9090               | 0.9110            | 0.9100        | 0.9824              |

| Metric                | Value  |
|:----------------------|:-------|
| f1-exact              | 0.9237 |
| f1-strict             | 0.9100 |
| f1-partial            | 0.9365 |
| f1-type-match         | 0.9277 |

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.36.0
- PyTorch: 2.0.0
- Datasets: 2.17.1
- Tokenizers: 0.15.0

## Citation

### BibTeX
```
@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```

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