SpanMarker(Arabic) with xlm-roberta-base on wikiann

This is a SpanMarker model trained on the wikiann dataset that can be used for Named Entity Recognition. This SpanMarker model uses xlm-roberta-base as the underlying encoder.

Model Details

Model Description

• Model Type: SpanMarker
• Encoder: xlm-roberta-base
• Maximum Sequence Length: 512 tokens
• Maximum Entity Length: 30 words
• Training Dataset: wikiann
• Languages: ace, af, als, am, an, ang, ar, arc, arz, as, ast, ay, az, ba, bar, be, bg, bh, bn, bo, br, bs, ca, cbk, cdo, ce, ceb, ckb, co, crh, cs, csb, cv, cy, da, de, diq, dv, el, eml, en, eo, es, et, eu, ext, fa, fi, fo, fr, frr, fur, fy, ga, gan, gd, gl, gn, gu, hak, he, hi, hr, hsb, hu, hy, ia, id, ig, ilo, io, is, it, ja, jbo, jv, ka, kk, km, kn, ko, ksh, ku, ky, la, lb, li, lij, lmo, ln, lt, lv, lzh, mg, mhr, mi, min, mk, ml, mn, mr, ms, mt, mwl, my, mzn, nan, nap, nds, ne, nl, nn, no, nov, oc, or, os, pa, pdc, pl, pms, pnb, ps, pt, qu, rm, ro, ru, rw, sa, sah, scn, sco, sd, sgs, sh, si, sk, sl, so, sq, sr, su, sv, sw, szl, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vec, vep, vi, vls, vo, vro, wa, war, wuu, xmf, yi, yo, yue, zea, zh
• License: other

Model Sources

• Repository: SpanMarker on GitHub
• Thesis: SpanMarker For Named Entity Recognition

Model Labels

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("موطنها بلاد الشام تركيا.")

Downstream Use

You can finetune this model on your own dataset.

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

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>

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Training Details

Training Set Metrics

Training Hyperparameters

• learning_rate: 1e-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: 10

Training Results

Framework Versions

• Python: 3.10.12
• SpanMarker: 1.4.0
• Transformers: 4.34.1
• PyTorch: 2.1.0+cu118
• Datasets: 2.14.6
• Tokenizers: 0.14.1

Citation

If you use this model, please cite:

@InProceedings{iahlt2023WikiANNArabicNER,
        author =      "iahlt",
        title =       "Arabic NER on WikiANN",
        year =        "2023",
        publisher =   "",
        location =    "",
      }

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