End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: mdeberta-v3-base-subjectivity-italian
+  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. -->
+
+# mdeberta-v3-base-subjectivity-italian
+
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7922
+- Macro F1: 0.7490
+- Macro P: 0.7409
+- Macro R: 0.7602
+- Subj F1: 0.6402
+- Subj P: 0.6020
+- Subj R: 0.6836
+- Accuracy: 0.7961
+
+## 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: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Macro P | Macro R | Subj F1 | Subj P | Subj R | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:-------:|:-------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 101  | 0.6818          | 0.5159   | 0.6822  | 0.5414  | 0.1827  | 0.6129 | 0.1073 | 0.7451   |
+| No log        | 2.0   | 202  | 0.5257          | 0.6735   | 0.6922  | 0.7465  | 0.5977  | 0.4567 | 0.8644 | 0.6912   |
+| No log        | 3.0   | 303  | 0.6269          | 0.7393   | 0.7355  | 0.7437  | 0.6209  | 0.6043 | 0.6384 | 0.7931   |
+| No log        | 4.0   | 404  | 0.6516          | 0.7448   | 0.7353  | 0.7597  | 0.6373  | 0.5885 | 0.6949 | 0.7901   |
+| 0.4326        | 5.0   | 505  | 0.7883          | 0.7463   | 0.7413  | 0.7522  | 0.6322  | 0.6105 | 0.6554 | 0.7976   |
+| 0.4326        | 6.0   | 606  | 0.7922          | 0.7490   | 0.7409  | 0.7602  | 0.6402  | 0.6020 | 0.6836 | 0.7961   |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.5.1+cu121
+- Datasets 3.3.1
+- Tokenizers 0.21.0

config.json

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+{
+  "_name_or_path": "microsoft/mdeberta-v3-base",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "OBJ",
+    "1": "SUBJ"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "OBJ": 0,
+    "SUBJ": 1
+  },
+  "layer_norm_eps": 1e-07,
+  "legacy": true,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 0,
+  "vocab_size": 251000
+}

model.safetensors

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+size 1115268200

training_args.bin

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