End of training

README.md

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+---
+license: cc-by-4.0
+base_model: allegro/herbert-large-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: herbert-large-cased_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. -->
+
+# herbert-large-cased_ner
+
+This model is a fine-tuned version of [allegro/herbert-large-cased](https://huggingface.co/allegro/herbert-large-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3281
+- Precision: 0.9354
+- Recall: 0.9326
+- F1: 0.9337
+- Accuracy: 0.9598
+
+## 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 | 0.2556 | 0.8915 | 0.8923 | 0.8918 | 0.9369 |
+| 0.311 | 2.0 | 876 | 0.1920 | 0.9101 | 0.9107 | 0.9102 | 0.9473 |
+| 0.1466 | 3.0 | 1314 | 0.2481 | 0.9050 | 0.9058 | 0.9048 | 0.9442 |
+| 0.093 | 4.0 | 1752 | 0.2565 | 0.9187 | 0.9276 | 0.9229 | 0.9537 |
+| 0.0584 | 5.0 | 2190 | 0.2620 | 0.9216 | 0.9306 | 0.9260 | 0.9543 |
+| 0.037 | 6.0 | 2628 | 0.2891 | 0.9263 | 0.9310 | 0.9282 | 0.9533 |
+| 0.0169 | 7.0 | 3066 | 0.3159 | 0.9288 | 0.9314 | 0.9300 | 0.9564 |
+| 0.0123 | 8.0 | 3504 | 0.3317 | 0.9359 | 0.9348 | 0.9345 | 0.9606 |
+| 0.0123 | 9.0 | 3942 | 0.3097 | 0.9357 | 0.9305 | 0.9327 | 0.9594 |
+| 0.0048 | 10.0 | 4380 | 0.3281 | 0.9354 | 0.9326 | 0.9337 | 0.9598 |
+
+
+### 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": "allegro/herbert-large-cased",
+ "architectures": [
+ "BertForTokenClassification"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "classifier_dropout": null,
+ "directionality": "bidi",
+ "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": 514,
+ "model_type": "bert",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "pad_token_id": 1,
+ "pooler_fc_size": 768,
+ "pooler_num_attention_heads": 12,
+ "pooler_num_fc_layers": 3,
+ "pooler_size_per_head": 128,
+ "pooler_type": "first_token_transform",
+ "position_embedding_type": "absolute",
+ "tokenizer_class": "HerbertTokenizerFast",
+ "torch_dtype": "float32",
+ "transformers_version": "4.42.4",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 50000
+}

model.safetensors

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+size 1416234796

training_args.bin

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