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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cassandra-themis/ner-phrases |
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model-index: |
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- name: lsg-ner-phrases-16384 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# lsg-ner-phrases-16384 |
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This model is a fine-tuned version of [lsg-base-16384-juri](https://huggingface.co/cassandra-themis/lsg-base-4096-juri) on the cassandra-themis/ner-phrases dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0058 |
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- New Sentence Precision: 0.9955 |
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- New Sentence Recall: 0.9932 |
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- New Sentence F1: 0.9943 |
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- New Sentence Number: 442 |
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- Overall Precision: 0.9955 |
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- Overall Recall: 0.9932 |
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- Overall F1: 0.9943 |
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- Overall Accuracy: 0.9996 |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline |
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import re |
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model_path = "cassandra-themis/lsg-ner-phrases-16384" |
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model = AutoModelForTokenClassification.from_pretrained(model_path, trust_remote_code=True, use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_auth_token=True) |
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ner_pipe = pipeline("token-classification", model=model, tokenizer=tokenizer) |
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document = "My document" |
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document_flattened = re.sub(r'(\s|\t|\n)+', r' ', document).strip() |
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prediction = ner_pipe(document_flattened, aggregation_strategy="simple") |
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sentences = [] |
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for i in range(len(prediction) - 1): |
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sentences.append(document_flattened[prediction[i]["start"]:prediction[i+1]["start"]].strip()) |
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print("\n".join(sentences)) |
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``` |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 150.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.11.6 |
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