add ner-her data

README.md

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+Hugging Face's logo
+---
+language: 
+- ar
+- de
+- en
+- es
+- fr
+- it
+- lv
+- nl
+- pt
+- zh
+- multilingual
+
+---
+# distilbert-base-multilingual-cased-ner-hrl
+## Model description
+**distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned  Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). 
+Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages
+## Intended uses & limitations
+#### How to use
+You can use this model with Transformers *pipeline* for NER.
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
+model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
+ner_results = nlp(example)
+print(ner_results)
+```
+#### Limitations and bias
+This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.  
+## Training data
+The training data for the 10 languages are from: 
+
+Arabic: [ANERcorp](https://github.com/EmnamoR/Arabic-named-entity-recognition)
+German: [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
+English: [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
+Spanish: [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
+French: [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
+Italian: []()
+Latvian: [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
+Dutch: [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
+Portuguese: [Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
+Chinese: [MSRA](https://huggingface.co/datasets/msra_ner)
+
+The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-PER |Beginning of a person’s name right after another person’s name
+I-PER |Person’s name
+B-ORG |Beginning of an organisation right after another organisation
+I-ORG |Organisation
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
+## Training procedure
+This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.
+
+

config.json

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+{
+  "_name_or_path": "distilbert-base-multilingual-cased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "O",
+    "1": "B-DATE",
+    "2": "I-DATE",
+    "3": "B-PER",
+    "4": "I-PER",
+    "5": "B-ORG",
+    "6": "I-ORG",
+    "7": "B-LOC",
+    "8": "I-LOC"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "B-DATE": 1,
+    "B-LOC": 7,
+    "B-ORG": 5,
+    "B-PER": 3,
+    "I-DATE": 2,
+    "I-LOC": 8,
+    "I-ORG": 6,
+    "I-PER": 4,
+    "O": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "vocab_size": 119547
+}

pytorch_model.bin

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "name_or_path": "distilbert-base-multilingual-cased"}

training_args.bin

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