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-english
+  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-english
+
+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: 0.5845
+- Macro F1: 0.7921
+- Macro P: 0.7952
+- Macro R: 0.7941
+- Subj F1: 0.7885
+- Subj P: 0.8364
+- Subj R: 0.7458
+- Accuracy: 0.7922
+
+## 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   | 52   | 0.6894          | 0.5697   | 0.7150  | 0.6233  | 0.4375  | 0.875  | 0.2917 | 0.6104   |
+| No log        | 2.0   | 104  | 0.4827          | 0.7785   | 0.7793  | 0.7782  | 0.7910  | 0.7782 | 0.8042 | 0.7792   |
+| No log        | 3.0   | 156  | 0.4996          | 0.7922   | 0.7931  | 0.7932  | 0.7931  | 0.8214 | 0.7667 | 0.7922   |
+| No log        | 4.0   | 208  | 0.5405          | 0.7986   | 0.8027  | 0.8008  | 0.7938  | 0.8483 | 0.7458 | 0.7987   |
+| No log        | 5.0   | 260  | 0.5838          | 0.7920   | 0.7978  | 0.7948  | 0.7848  | 0.8495 | 0.7292 | 0.7922   |
+| No log        | 6.0   | 312  | 0.5845          | 0.7921   | 0.7952  | 0.7941  | 0.7885  | 0.8364 | 0.7458 | 0.7922   |
+
+
+### 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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