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@@ -6,7 +6,7 @@ Here you can find our NER models:
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  |model name | model description | model path | datasets |
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  |:----------|:------------------|:-----------|:--------:|
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  | **Basic** | Basic training on IAHALT | [https://huggingface.co/FusioNER/Basic_IAHALT](https://huggingface.co/FusioNER/Basic_IAHALT) | IAHALT |
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- | **Vitaly** | Vitaly training on IAHALT (with BI-BI problem) | [https://huggingface.co/FusioNER/Vitaly_NER](https://huggingface.co/FusioNER/Vitaly_NER) | IAHALT |
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  | **Name-Sentences** | Training on IAHALT + Name-Sentences[1] | [https://huggingface.co/FusioNER/Name-Sentences](https://huggingface.co/FusioNER/Name-Sentences) | IAHALT |
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  | **Entity-Injection** | Training on IAHALT + Entity-Injection[2] | [https://huggingface.co/FusioNER/Entity-Injection](https://huggingface.co/FusioNER/Entity-Injection) | IAHALT |
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  | **Smart_Injection** | Training on IAHALT + Name-Sentences[1] + Entity-Injection[2] | [https://huggingface.co/FusioNER/Smart_Injection](https://huggingface.co/FusioNER/Smart_Injection) | IAHALT |
@@ -21,3 +21,9 @@ Here you can find our NER models:
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  [2] **Entity-Injection**: Replace a tagged entity in the original corpus with a new entity. By using, this method, the model can learn new entities (not labels!) which the model not extracted before.
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  |model name | model description | model path | datasets |
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  |:----------|:------------------|:-----------|:--------:|
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  | **Basic** | Basic training on IAHALT | [https://huggingface.co/FusioNER/Basic_IAHALT](https://huggingface.co/FusioNER/Basic_IAHALT) | IAHALT |
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+ | **Vitaly** | Vitaly training on IAHALT (with BI-BI problem[3]) | [https://huggingface.co/FusioNER/Vitaly_NER](https://huggingface.co/FusioNER/Vitaly_NER) | IAHALT |
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  | **Name-Sentences** | Training on IAHALT + Name-Sentences[1] | [https://huggingface.co/FusioNER/Name-Sentences](https://huggingface.co/FusioNER/Name-Sentences) | IAHALT |
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  | **Entity-Injection** | Training on IAHALT + Entity-Injection[2] | [https://huggingface.co/FusioNER/Entity-Injection](https://huggingface.co/FusioNER/Entity-Injection) | IAHALT |
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  | **Smart_Injection** | Training on IAHALT + Name-Sentences[1] + Entity-Injection[2] | [https://huggingface.co/FusioNER/Smart_Injection](https://huggingface.co/FusioNER/Smart_Injection) | IAHALT |
 
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  [2] **Entity-Injection**: Replace a tagged entity in the original corpus with a new entity. By using, this method, the model can learn new entities (not labels!) which the model not extracted before.
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+ [3] **BI-BI Problem**: Building training corpus when entities from the same type appear in sequence, labeled as continuations of one another.
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+ For example, the text "Harry Potter and Ron Weasley" would tagged as **SINGLE** entity. That problem prevent the model to extract entities correctly.
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+ **MIT License**