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README.md
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@@ -37,6 +37,16 @@ Install or update the gliner package:
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pip install gliner -U
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```
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Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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```python
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@@ -92,37 +102,39 @@ outputs = model.batch_predict_with_embeds(texts, entity_embeddings, labels)
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### Benchmarks
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Below you can see the table with benchmarking results on various named entity recognition datasets:
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| Dataset | Score |
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|-------------------------|--------|
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| ACE 2004 |
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| ACE 2005 |
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| AnatEM |
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| Broad Tweet Corpus |
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| CoNLL 2003 | 60.
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| FabNER
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| FindVehicle |
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| GENIA_NER |
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| HarveyNER |
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| MultiNERD |
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| Ontonotes | 28.
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| PolyglotNER |
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| TweetNER7 | 35.
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| WikiANN en | 53.
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| WikiNeural |
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| bc2gm |
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| bc4chemd |
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| bc5cdr | 71.
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| ncbi |
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| **Average** | **
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| CrossNER_AI |
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| CrossNER_literature |
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| CrossNER_music |
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| CrossNER_politics |
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| CrossNER_science |
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| mit-movie |
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| mit-restaurant |
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| **Average (zero-shot benchmark)** | **
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### Join Our Discord
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pip install gliner -U
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```
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And LLM2Vec package:
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```bash
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pip install llm2vec
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```
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To use this particular Qwen-based model you need different `transformers` package version than llm2vec requires, so install it manually:
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```bash
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pip install transformers==4.44.1
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```
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Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
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```python
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### Benchmarks
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Below you can see the table with benchmarking results on various named entity recognition datasets:
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Here’s the updated table with your new data:
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| Dataset | Score |
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|-------------------------|--------|
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| ACE 2004 | 31.5% |
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| ACE 2005 | 31.5% |
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| AnatEM | 43.4% |
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| Broad Tweet Corpus | 55.6% |
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| CoNLL 2003 | 60.1% |
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| FabNER | 23.9% |
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| FindVehicle | 30.2% |
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| GENIA_NER | 50.7% |
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| HarveyNER | 16.9% |
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| MultiNERD | 53.3% |
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| Ontonotes | 28.1% |
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| PolyglotNER | 39.2% |
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| TweetNER7 | 35.3% |
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| WikiANN en | 53.2% |
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| WikiNeural | 65.0% |
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| bc2gm | 56.3% |
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| bc4chemd | 54.4% |
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| bc5cdr | 71.0% |
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| ncbi | 63.7% |
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| **Average** | **45.4%** |
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| CrossNER_AI | 54.0% |
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| CrossNER_literature | 64.4% |
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| CrossNER_music | 63.0% |
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| CrossNER_politics | 69.3% |
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| CrossNER_science | 64.2% |
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| mit-movie | 52.7% |
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| mit-restaurant | 37.6% |
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| **Average (zero-shot benchmark)** | **57.9%** |
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### Join Our Discord
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