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cause-bert-because

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alenatz/cause-BERT-BECAUSE

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  1. README.md +72 -0
  2. config.json +34 -0
  3. model.safetensors +3 -0
  4. runs/Jul03_21-54-26_ccoon/events.out.tfevents.1720036468.ccoon.26033.1 +3 -0
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: bert-base-cased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: filtered_cause_extraction_bert_because
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+ results: []
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+ ---
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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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+
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+ # filtered_cause_extraction_bert_because
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+
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4768
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+ - Precision: 0.25
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+ - Recall: 0.3878
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+ - F1: 0.304
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+ - Accuracy: 0.8087
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+ - Cause P: 0.25
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+ - Cause R: 0.3878
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+ - Cause F1: 0.304
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 4e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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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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+ - num_epochs: 2
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Cause P | Cause R | Cause F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:-------:|:--------:|
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+ | 0.7298 | 0.41 | 20 | 0.5714 | 0.0843 | 0.3010 | 0.1317 | 0.6191 | 0.0843 | 0.3010 | 0.1317 |
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+ | 0.7298 | 0.82 | 40 | 0.4815 | 0.1528 | 0.3010 | 0.2027 | 0.7796 | 0.1528 | 0.3010 | 0.2027 |
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+ | 0.7298 | 1.22 | 60 | 0.4449 | 0.2061 | 0.3776 | 0.2667 | 0.7979 | 0.2061 | 0.3776 | 0.2667 |
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+ | 0.7298 | 1.63 | 80 | 0.4607 | 0.2444 | 0.3929 | 0.3014 | 0.8052 | 0.2444 | 0.3929 | 0.3014 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.3.1.post100
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+ - Datasets 2.20.0
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+ - Tokenizers 0.15.1
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.37.2",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 28996
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