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_extra_data
+  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_extra_data
+
+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: 0.0089
+- Chemical: {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969}
+- Micro avg: {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969}
+- Macro avg: {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969}
+- Weighted avg: {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969}
+
+## 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
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Chemical                                                                                                        | Micro avg                                                                                                       | Macro avg                                                                                                       | Weighted avg                                                                                                    |
+|:-------------:|:------:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|
+| 0.0265        | 1.0000 | 16214 | 0.0121          | {'precision': 0.933605720122574, 'recall': 0.9432404540763674, 'f1-score': 0.9383983572895276, 'support': 969}  | {'precision': 0.933605720122574, 'recall': 0.9432404540763674, 'f1-score': 0.9383983572895276, 'support': 969}  | {'precision': 0.933605720122574, 'recall': 0.9432404540763674, 'f1-score': 0.9383983572895276, 'support': 969}  | {'precision': 0.933605720122574, 'recall': 0.9432404540763674, 'f1-score': 0.9383983572895277, 'support': 969}  |
+| 0.009         | 2.0    | 32429 | 0.0090          | {'precision': 0.9571865443425076, 'recall': 0.9690402476780186, 'f1-score': 0.963076923076923, 'support': 969}  | {'precision': 0.9571865443425076, 'recall': 0.9690402476780186, 'f1-score': 0.963076923076923, 'support': 969}  | {'precision': 0.9571865443425076, 'recall': 0.9690402476780186, 'f1-score': 0.963076923076923, 'support': 969}  | {'precision': 0.9571865443425076, 'recall': 0.9690402476780186, 'f1-score': 0.963076923076923, 'support': 969}  |
+| 0.0053        | 2.9999 | 48642 | 0.0089          | {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969} | {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969} | {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969} | {'precision': 0.9699792960662525, 'recall': 0.9669762641898865, 'f1-score': 0.9684754521963824, 'support': 969} |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,37 @@
+{
+  "_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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runs/Feb24_13-10-06_ip-10-3-48-151.eu-central-1.compute.internal/events.out.tfevents.1740402839.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
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+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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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,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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