End of training

README.md

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+---
+base_model: plncmm/beto-clinical-wl-es
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: beto-clinical-wl-es-ner
+  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. -->
+
+# beto-clinical-wl-es-ner
+
+This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3299
+- Precision: 0.8665
+- Recall: 0.9037
+- F1: 0.8847
+- Accuracy: 0.9418
+
+## 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: 2e-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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 280  | 0.2544          | 0.8328    | 0.8489 | 0.8408 | 0.9247   |
+| 0.3847        | 2.0   | 560  | 0.2645          | 0.8170    | 0.8667 | 0.8411 | 0.9236   |
+| 0.3847        | 3.0   | 840  | 0.2372          | 0.8512    | 0.8726 | 0.8617 | 0.9338   |
+| 0.1056        | 4.0   | 1120 | 0.2749          | 0.8403    | 0.8963 | 0.8674 | 0.9327   |
+| 0.1056        | 5.0   | 1400 | 0.2895          | 0.8557    | 0.9052 | 0.8798 | 0.9354   |
+| 0.057         | 6.0   | 1680 | 0.2630          | 0.8707    | 0.9081 | 0.8891 | 0.9408   |
+| 0.057         | 7.0   | 1960 | 0.2759          | 0.8614    | 0.9022 | 0.8813 | 0.9418   |
+| 0.031         | 8.0   | 2240 | 0.3099          | 0.8689    | 0.9037 | 0.8860 | 0.9408   |
+| 0.0222        | 9.0   | 2520 | 0.3506          | 0.8597    | 0.9081 | 0.8833 | 0.9386   |
+| 0.0222        | 10.0  | 2800 | 0.2962          | 0.8693    | 0.8963 | 0.8826 | 0.9421   |
+| 0.0169        | 11.0  | 3080 | 0.3218          | 0.8709    | 0.8993 | 0.8848 | 0.9432   |
+| 0.0169        | 12.0  | 3360 | 0.3459          | 0.8672    | 0.9096 | 0.8879 | 0.9400   |
+| 0.0134        | 13.0  | 3640 | 0.3299          | 0.8661    | 0.9007 | 0.8831 | 0.9413   |
+| 0.0134        | 14.0  | 3920 | 0.3318          | 0.8707    | 0.9081 | 0.8891 | 0.9429   |
+| 0.0126        | 15.0  | 4200 | 0.3299          | 0.8665    | 0.9037 | 0.8847 | 0.9418   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "plncmm/beto-clinical-wl-es",
+  "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": "O",
+    "1": "B-Anatomical_Entity",
+    "10": "I-Location",
+    "11": "B-Type_of_measure",
+    "12": "I-Type_of_measure",
+    "13": "B-Measure",
+    "14": "I-Measure",
+    "15": "B-Degree",
+    "16": "I-Degree",
+    "17": "B-ConditionalTemporal",
+    "18": "I-ConditionalTemporal",
+    "2": "I-Anatomical_Entity",
+    "3": "B-Finding",
+    "4": "I-Finding",
+    "5": "B-Uncertainty",
+    "6": "I-Uncertainty",
+    "7": "B-Negation",
+    "8": "I-Negation",
+    "9": "B-Location"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-Anatomical_Entity": "1",
+    "B-ConditionalTemporal": "17",
+    "B-Degree": "15",
+    "B-Finding": "3",
+    "B-Location": "9",
+    "B-Measure": "13",
+    "B-Negation": "7",
+    "B-Type_of_measure": "11",
+    "B-Uncertainty": "5",
+    "I-Anatomical_Entity": "2",
+    "I-ConditionalTemporal": "18",
+    "I-Degree": "16",
+    "I-Finding": "4",
+    "I-Location": "10",
+    "I-Measure": "14",
+    "I-Negation": "8",
+    "I-Type_of_measure": "12",
+    "I-Uncertainty": "6",
+    "O": "0"
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31002
+}

model.safetensors

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runs/Jun02_18-43-24_167d16a24718/events.out.tfevents.1717353804.167d16a24718.192.1

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special_tokens_map.json

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+    "lstrip": false,
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+    "single_word": false
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+}

tokenizer_config.json

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

training_args.bin

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