End of training

README.md

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+---
+base_model: cardiffnlp/twitter-roberta-base-irony
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: Twroberta-baseB_10epoch
+  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. -->
+
+# Twroberta-baseB_10epoch
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-irony](https://huggingface.co/cardiffnlp/twitter-roberta-base-irony) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1763
+- Accuracy: 0.7771
+- Precision: 0.2366
+- Recall: 0.3137
+- F1: 0.2679
+
+## 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: 16
+- 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 | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 1.0   | 217  | 0.1251          | 0.8571   | 0.0       | 0.0    | 0.0    |
+| No log        | 2.0   | 434  | 0.1213          | 0.8571   | 0.0       | 0.0    | 0.0    |
+| 0.1617        | 3.0   | 651  | 0.1226          | 0.8157   | 0.2655    | 0.3026 | 0.2828 |
+| 0.1617        | 4.0   | 868  | 0.1423          | 0.7671   | 0.1991    | 0.2989 | 0.2389 |
+| 0.0899        | 5.0   | 1085 | 0.1594          | 0.7364   | 0.2142    | 0.3727 | 0.2695 |
+| 0.0899        | 6.0   | 1302 | 0.1560          | 0.8086   | 0.2567    | 0.2214 | 0.2320 |
+| 0.0411        | 7.0   | 1519 | 0.1963          | 0.715    | 0.1945    | 0.3875 | 0.2584 |
+| 0.0411        | 8.0   | 1736 | 0.1687          | 0.7914   | 0.2520    | 0.2804 | 0.2601 |
+| 0.0411        | 9.0   | 1953 | 0.1726          | 0.7843   | 0.2419    | 0.2989 | 0.2646 |
+| 0.0197        | 10.0  | 2170 | 0.1763          | 0.7771   | 0.2366    | 0.3137 | 0.2679 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "cardiffnlp/twitter-roberta-base-irony",
+  "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": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5
+  },
+  "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": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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runs/Jun06_11-16-29_b9092328a211/events.out.tfevents.1717672590.b9092328a211.7542.12

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special_tokens_map.json

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+{
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+}

tokenizer_config.json

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+  "add_prefix_space": false,
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

training_args.bin

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