Multilingual-Perspectivist-NLU/irony_de_Austria

README.md

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+---
+license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: irony_de_Austria
+  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. -->
+
+# irony_de_Austria
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0034
+- Accuracy: 0.6376
+- Precision: 0.4444
+- Recall: 0.7299
+- F1: 0.5525
+
+## 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-06
+- train_batch_size: 16
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.0044        | 1.0   | 84   | 0.0042          | 0.5928   | 0.3953    | 0.6204 | 0.4830 |
+| 0.0041        | 2.0   | 168  | 0.0040          | 0.5638   | 0.3858    | 0.7153 | 0.5013 |
+| 0.0038        | 3.0   | 252  | 0.0039          | 0.4966   | 0.3562    | 0.7956 | 0.4921 |
+| 0.0037        | 4.0   | 336  | 0.0034          | 0.6577   | 0.4592    | 0.6569 | 0.5405 |
+| 0.0032        | 5.0   | 420  | 0.0032          | 0.6600   | 0.4603    | 0.6350 | 0.5337 |
+| 0.0031        | 6.0   | 504  | 0.0032          | 0.5213   | 0.3729    | 0.8248 | 0.5136 |
+| 0.0029        | 7.0   | 588  | 0.0031          | 0.5884   | 0.4064    | 0.7445 | 0.5258 |
+| 0.0024        | 8.0   | 672  | 0.0029          | 0.5839   | 0.4089    | 0.8029 | 0.5419 |
+| 0.002         | 9.0   | 756  | 0.0032          | 0.6465   | 0.4483    | 0.6642 | 0.5353 |
+| 0.0021        | 10.0  | 840  | 0.0028          | 0.6331   | 0.4395    | 0.7153 | 0.5444 |
+| 0.0016        | 11.0  | 924  | 0.0038          | 0.6823   | 0.4855    | 0.6131 | 0.5419 |
+| 0.0009        | 12.0  | 1008 | 0.0034          | 0.6376   | 0.4444    | 0.7299 | 0.5525 |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.5
+- Tokenizers 0.14.1

config.json

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+{
+  "_name_or_path": "roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "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": "4.34.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

pytorch_model.bin

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+size 498657649

training_args.bin

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