End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: alvaroalon2/biobert_chemical_ner
+tags:
+- generated_from_trainer
+model-index:
+- name: murat_chem_model
+ results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# murat_chem_model
+
+This model is a fine-tuned version of [alvaroalon2/biobert_chemical_ner](https://huggingface.co/alvaroalon2/biobert_chemical_ner) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3827
+- Chemical: {'precision': 0.8843176605504587, 'recall': 0.8439124487004104, 'f1': 0.863642727145457, 'number': 7310}
+- Overall Precision: 0.8843
+- Overall Recall: 0.8439
+- Overall F1: 0.8636
+- Overall Accuracy: 0.9447
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Chemical | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.6817 | 1.2048 | 100 | 0.3090 | {'precision': 0.8702002710435175, 'recall': 0.7905608755129959, 'f1': 0.828471077342126, 'number': 7310} | 0.8702 | 0.7906 | 0.8285 | 0.9204 |
+| 1.6817 | 2.4096 | 200 | 0.2900 | {'precision': 0.8720861611094718, 'recall': 0.8086183310533516, 'f1': 0.8391538898353209, 'number': 7310} | 0.8721 | 0.8086 | 0.8392 | 0.9312 |
+| 1.6817 | 3.6145 | 300 | 0.3071 | {'precision': 0.8830255057167986, 'recall': 0.824076607387141, 'f1': 0.8525332578545147, 'number': 7310} | 0.8830 | 0.8241 | 0.8525 | 0.9398 |
+| 1.6817 | 4.8193 | 400 | 0.3099 | {'precision': 0.882646325363063, 'recall': 0.8231190150478797, 'f1': 0.8518439866921499, 'number': 7310} | 0.8826 | 0.8231 | 0.8518 | 0.9411 |
+| 0.1018 | 6.0241 | 500 | 0.3891 | {'precision': 0.8816456613066782, 'recall': 0.8325581395348837, 'f1': 0.8563990712727785, 'number': 7310} | 0.8816 | 0.8326 | 0.8564 | 0.9402 |
+| 0.1018 | 7.2289 | 600 | 0.3672 | {'precision': 0.8851640513552068, 'recall': 0.8488372093023255, 'f1': 0.8666201117318435, 'number': 7310} | 0.8852 | 0.8488 | 0.8666 | 0.9451 |
+| 0.1018 | 8.4337 | 700 | 0.3459 | {'precision': 0.8812949640287769, 'recall': 0.8378932968536251, 'f1': 0.8590462833099579, 'number': 7310} | 0.8813 | 0.8379 | 0.8590 | 0.9449 |
+| 0.1018 | 9.6386 | 800 | 0.3601 | {'precision': 0.880656108597285, 'recall': 0.8519835841313269, 'f1': 0.8660826032540675, 'number': 7310} | 0.8807 | 0.8520 | 0.8661 | 0.9462 |
+| 0.1018 | 10.8434 | 900 | 0.3711 | {'precision': 0.881471972614463, 'recall': 0.8454172366621067, 'f1': 0.8630682214929124, 'number': 7310} | 0.8815 | 0.8454 | 0.8631 | 0.9443 |
+| 0.0038 | 12.0482 | 1000 | 0.3779 | {'precision': 0.8816542644533486, 'recall': 0.8428180574555404, 'f1': 0.8617988529864317, 'number': 7310} | 0.8817 | 0.8428 | 0.8618 | 0.9437 |
+| 0.0038 | 13.2530 | 1100 | 0.3829 | {'precision': 0.8837275985663082, 'recall': 0.8432284541723666, 'f1': 0.863003150157508, 'number': 7310} | 0.8837 | 0.8432 | 0.8630 | 0.9447 |
+| 0.0038 | 14.4578 | 1200 | 0.3827 | {'precision': 0.8843176605504587, 'recall': 0.8439124487004104, 'f1': 0.863642727145457, 'number': 7310} | 0.8843 | 0.8439 | 0.8636 | 0.9447 |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

config.json

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+{
+ "_name_or_path": "alvaroalon2/biobert_chemical_ner",
+ "_num_labels": 3,
+ "architectures": [
+ "BertForTokenClassification"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "classifier_dropout": null,
+ "gradient_checkpointing": false,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "id2label": {
+ "0": "B-CHEMICAL",
+ "1": "I-CHEMICAL",
+ "2": "O"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "label2id": {
+ "B-CHEMICAL": 0,
+ "I-CHEMICAL": 1,
+ "O": 2
+ },
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 512,
+ "model_type": "bert",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pad_token_id": 0,
+ "position_embedding_type": "absolute",
+ "torch_dtype": "float32",
+ "transformers_version": "4.44.2",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 28996
+}

model.safetensors

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+size 430911284

runs/Sep09_13-39-20_ip-10-3-48-151.eu-central-1.compute.internal/events.out.tfevents.1725889166.ip-10-3-48-151.eu-central-1.compute.internal

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special_tokens_map.json

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

tokenizer_config.json

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+{
+ "added_tokens_decoder": {
+ "0": {
+ "content": "[PAD]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "100": {
+ "content": "[UNK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
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+ "content": "[CLS]",
+ "lstrip": false,
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+ "rstrip": false,
+ "single_word": false,
+ "special": true
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+ "103": {
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+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "[CLS]",
+ "do_basic_tokenize": true,
+ "do_lower_case": false,
+ "init_inputs": [],
+ "mask_token": "[MASK]",
+ "max_len": 512,
+ "model_max_length": 512,
+ "never_split": null,
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "special_tokens": true,
+ "strip_accents": null,
+ "tokenize_chinese_chars": true,
+ "tokenizer_class": "BertTokenizer",
+ "unk_token": "[UNK]"
+}

training_args.bin

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