End of training

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,387 @@
+---
+language:
+- 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
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+- 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
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: جامعة بيزا (إيطاليا).
+- text: تعلم في جامعة أوكسفورد، جامعة برنستون، جامعة كولومبيا.
+- text: موطنها بلاد الشام تركيا.
+- text: عادل إمام - نور الشريف
+- text: فوكسي و بورتشا ضد مونكي دي لوفي و نامي
+pipeline_tag: token-classification
+base_model: xlm-roberta-base
+model-index:
+- name: SpanMarker with xlm-roberta-base on wikiann
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: wikiann
+      split: eval
+    metrics:
+    - type: f1
+      value: 0.8965362325351544
+      name: F1
+    - type: precision
+      value: 0.9077510917030568
+      name: Precision
+    - type: recall
+      value: 0.8855951007366646
+      name: Recall
+---
+
+# SpanMarker with xlm-roberta-base on wikiann
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [wikiann](https://huggingface.co/datasets/wikiann) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
+- **Maximum Sequence Length:** 512 tokens
+- **Maximum Entity Length:** 30 words
+- **Training Dataset:** [wikiann](https://huggingface.co/datasets/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](https://github.com/tomaarsen/SpanMarkerNER)
+- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
+
+### Model Labels
+| Label | Examples                                                               |
+|:------|:-----------------------------------------------------------------------|
+| LOC   | "شور بلاغ ( مقاطعة غرمي )", "دهنو ( تایباد )", "أقاليم ما وراء البحار" |
+| ORG   | "الحزب الاشتراكي", "نادي باسوش دي فيريرا", "دايو ( شركة )"             |
+| PER   | "فرنسوا ميتيران،", "ديفيد نالبانديان", "حكم ( كرة قدم )"               |
+
+## 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("موطنها بلاد الشام تركيا.")
+```
+
+### 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       | 3   | 6.4592 | 63  |
+| Entities per sentence | 1   | 1.1251 | 13  |
+
+### 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
+| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.1989 | 500   | 0.1735          | 0.2667               | 0.0011            | 0.0021        | 0.4103              |
+| 0.3979 | 1000  | 0.0808          | 0.7283               | 0.5314            | 0.6145        | 0.7716              |
+| 0.5968 | 1500  | 0.0595          | 0.7876               | 0.6872            | 0.7340        | 0.8546              |
+| 0.7957 | 2000  | 0.0532          | 0.8148               | 0.7600            | 0.7865        | 0.8823              |
+| 0.9946 | 2500  | 0.0478          | 0.8485               | 0.8028            | 0.8250        | 0.9085              |
+| 1.1936 | 3000  | 0.0419          | 0.8586               | 0.8084            | 0.8327        | 0.9101              |
+| 1.3925 | 3500  | 0.0390          | 0.8628               | 0.8367            | 0.8495        | 0.9237              |
+| 1.5914 | 4000  | 0.0456          | 0.8559               | 0.8299            | 0.8427        | 0.9231              |
+| 1.7903 | 4500  | 0.0375          | 0.8682               | 0.8469            | 0.8574        | 0.9282              |
+| 1.9893 | 5000  | 0.0323          | 0.8821               | 0.8635            | 0.8727        | 0.9348              |
+| 2.1882 | 5500  | 0.0346          | 0.8781               | 0.8632            | 0.8706        | 0.9346              |
+| 2.3871 | 6000  | 0.0318          | 0.8953               | 0.8523            | 0.8733        | 0.9345              |
+| 2.5860 | 6500  | 0.0311          | 0.8861               | 0.8691            | 0.8775        | 0.9373              |
+| 2.7850 | 7000  | 0.0323          | 0.89                 | 0.8689            | 0.8793        | 0.9383              |
+| 2.9839 | 7500  | 0.0310          | 0.8892               | 0.8780            | 0.8836        | 0.9419              |
+| 3.1828 | 8000  | 0.0320          | 0.8817               | 0.8762            | 0.8790        | 0.9397              |
+| 3.3817 | 8500  | 0.0291          | 0.8981               | 0.8778            | 0.8878        | 0.9438              |
+| 3.5807 | 9000  | 0.0336          | 0.8972               | 0.8792            | 0.8881        | 0.9450              |
+| 3.7796 | 9500  | 0.0323          | 0.8927               | 0.8757            | 0.8841        | 0.9424              |
+| 3.9785 | 10000 | 0.0315          | 0.9028               | 0.8748            | 0.8886        | 0.9436              |
+| 4.1774 | 10500 | 0.0330          | 0.8984               | 0.8855            | 0.8919        | 0.9458              |
+| 4.3764 | 11000 | 0.0315          | 0.9023               | 0.8844            | 0.8933        | 0.9469              |
+| 4.5753 | 11500 | 0.0305          | 0.9029               | 0.8886            | 0.8957        | 0.9486              |
+| 4.6171 | 11605 | 0.0323          | 0.9078               | 0.8856            | 0.8965        | 0.9487              |
+
+### 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
+
+### 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.*
+-->

config.json

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+  ],
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+    "add_cross_attention": false,
+    "architectures": [
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+    "hidden_size": 768,
+    "id2label": {
+      "0": "O",
+      "1": "B-PER",
+      "2": "I-PER",
+      "3": "B-ORG",
+      "4": "I-ORG",
+      "5": "B-LOC",
+      "6": "I-LOC"
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+    "top_k": 50,
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+    "use_cache": true,
+    "vocab_size": 250008
+  },
+  "entity_max_length": 30,
+  "id2label": {
+    "0": "O",
+    "1": "LOC",
+    "2": "ORG",
+    "3": "PER"
+  },
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+    "O": 0,
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+    "PER": 3
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+  "marker_max_length": 128,
+  "max_next_context": null,
+  "max_prev_context": null,
+  "model_max_length": 512,
+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.4.0",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.34.1",
+  "vocab_size": 250008
+}

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