End of training

README.md

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+---
+license: apache-2.0
+base_model: projecte-aina/roberta-base-ca-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: FS_25_05
+  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. -->
+
+# FS_25_05
+
+This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1830
+- Accuracy: 0.9608
+- Precision: 0.9618
+- Recall: 0.9606
+- F1: 0.9603
+- Ratio: 0.0510
+
+## 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
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Ratio  |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
+| 1.7701        | 1.0   | 362  | 1.6423          | 0.8980   | 0.9086    | 0.8982 | 0.8983 | 0.0549 |
+| 0.3762        | 2.0   | 724  | 0.3326          | 0.9451   | 0.9466    | 0.9449 | 0.9446 | 0.0529 |
+| 0.1056        | 3.0   | 1086 | 0.1995          | 0.9608   | 0.9617    | 0.9608 | 0.9605 | 0.0510 |
+| 0.0238        | 4.0   | 1448 | 0.1830          | 0.9608   | 0.9618    | 0.9606 | 0.9603 | 0.0510 |
+| 0.1092        | 5.0   | 1810 | 0.1919          | 0.9627   | 0.9641    | 0.9627 | 0.9626 | 0.0510 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "projecte-aina/roberta-base-ca-v2",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Aigues",
+    "1": "Consum, comer\u00e7 i mercats",
+    "2": "Cultura",
+    "3": "Economia",
+    "4": "Educaci\u00f3",
+    "5": "Enllumenat p\u00fablic",
+    "6": "Esports",
+    "7": "Habitatge",
+    "8": "Horta",
+    "9": "Informaci\u00f3 general",
+    "10": "Inform\u00e0tica",
+    "11": "Joventut",
+    "12": "Medi ambient",
+    "13": "Neteja de la via p\u00fablica",
+    "14": "Salut p\u00fablica i Cementiri",
+    "15": "Seguretat",
+    "16": "Serveis socials",
+    "17": "Tramitacions",
+    "18": "Urbanisme",
+    "19": "Via p\u00fablica i mobilitat"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50262
+}

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