End of training

README.md

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+---
+license: mit
+base_model: deepset/gbert-large
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: gbert-large_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. -->
+
+# gbert-large_ner
+
+This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3755
+- Precision: 0.9010
+- Recall: 0.8948
+- F1: 0.8975
+- Accuracy: 0.9521
+
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 438  | 0.2334          | 0.8727    | 0.8653 | 0.8649 | 0.9303   |
+| 0.3598        | 2.0   | 876  | 0.2149          | 0.8885    | 0.8649 | 0.8757 | 0.9391   |
+| 0.1678        | 3.0   | 1314 | 0.2257          | 0.8820    | 0.8906 | 0.8847 | 0.9461   |
+| 0.1054        | 4.0   | 1752 | 0.2580          | 0.8902    | 0.8884 | 0.8884 | 0.9463   |
+| 0.0645        | 5.0   | 2190 | 0.2881          | 0.8896    | 0.8820 | 0.8833 | 0.9451   |
+| 0.0436        | 6.0   | 2628 | 0.2767          | 0.8922    | 0.8911 | 0.8914 | 0.9479   |
+| 0.0245        | 7.0   | 3066 | 0.3190          | 0.9026    | 0.9038 | 0.9030 | 0.9534   |
+| 0.0108        | 8.0   | 3504 | 0.3547          | 0.8879    | 0.8886 | 0.8876 | 0.9474   |
+| 0.0108        | 9.0   | 3942 | 0.3780          | 0.8943    | 0.8886 | 0.8910 | 0.9494   |
+| 0.0074        | 10.0  | 4380 | 0.3755          | 0.9010    | 0.8948 | 0.8975 | 0.9521   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "deepset/gbert-large",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}

model.safetensors

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+size 1338820348

training_args.bin

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