End of training

README.md

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+---
+library_name: transformers
+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-topic_classification
+  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-topic_classification
+
+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: 0.5731
+- Precision: 0.9195
+- Recall: 0.9014
+- F1: 0.9082
+- Accuracy: 0.9167
+
+## 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   | 44   | 0.3576          | 0.9119    | 0.8684 | 0.8815 | 0.9020   |
+| No log        | 2.0   | 88   | 0.3342          | 0.9085    | 0.9027 | 0.8973 | 0.9069   |
+| No log        | 3.0   | 132  | 0.4985          | 0.9121    | 0.8826 | 0.8916 | 0.9020   |
+| No log        | 4.0   | 176  | 0.6182          | 0.8998    | 0.8858 | 0.8911 | 0.9020   |
+| No log        | 5.0   | 220  | 0.5089          | 0.9056    | 0.8880 | 0.8944 | 0.9020   |
+| No log        | 6.0   | 264  | 0.6806          | 0.9061    | 0.8593 | 0.8766 | 0.8922   |
+| No log        | 7.0   | 308  | 0.5604          | 0.9127    | 0.8866 | 0.8969 | 0.9069   |
+| No log        | 8.0   | 352  | 0.5780          | 0.9157    | 0.9036 | 0.9077 | 0.9167   |
+| No log        | 9.0   | 396  | 0.5733          | 0.9195    | 0.9014 | 0.9082 | 0.9167   |
+| No log        | 10.0  | 440  | 0.5731          | 0.9195    | 0.9014 | 0.9082 | 0.9167   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "allegro/herbert-large-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "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",
+  "problem_type": "single_label_classification",
+  "tokenizer_class": "HerbertTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

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training_args.bin

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