Model save

README.md

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+---
+license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- poem_sentiment
+metrics:
+- accuracy
+model-index:
+- name: poem_sentiment
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: poem_sentiment
+      type: poem_sentiment
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8857142857142857
+---
+
+<!-- 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. -->
+
+# poem_sentiment
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the poem_sentiment dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4747
+- 0: {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19}
+- 1: {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17}
+- 2: {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69}
+- Accuracy: 0.8857
+- Macro avg: {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105}
+- Weighted avg: {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105}
+
+## 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 | 0                                                                                                              | 1                                                                                                              | 2                                                                                                              | Accuracy | Macro avg                                                                                                         | Weighted avg                                                                                                     |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|
+| 1.0922        | 1.0   | 112  | 0.8825          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 19}                                              | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 17}                                              | {'precision': 0.6571428571428571, 'recall': 1.0, 'f1-score': 0.7931034482758621, 'support': 69}                | 0.6571   | {'precision': 0.21904761904761905, 'recall': 0.3333333333333333, 'f1-score': 0.26436781609195403, 'support': 105} | {'precision': 0.43183673469387757, 'recall': 0.6571428571428571, 'f1-score': 0.5211822660098522, 'support': 105} |
+| 0.6877        | 2.0   | 224  | 0.4747          | {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19}                | {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17} | {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69} | 0.8857   | {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105}   | {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105}  |
+| 0.5299        | 3.0   | 336  | 0.6595          | {'precision': 0.8, 'recall': 0.8421052631578947, 'f1-score': 0.8205128205128205, 'support': 19}                | {'precision': 1.0, 'recall': 0.4117647058823529, 'f1-score': 0.5833333333333334, 'support': 17}                | {'precision': 0.8461538461538461, 'recall': 0.9565217391304348, 'f1-score': 0.8979591836734695, 'support': 69} | 0.8476   | {'precision': 0.882051282051282, 'recall': 0.7367972360568942, 'f1-score': 0.7672684458398744, 'support': 105}    | {'precision': 0.8627106227106227, 'recall': 0.8476190476190476, 'f1-score': 0.8330056564750442, 'support': 105}  |
+| 0.9027        | 4.0   | 448  | 0.5981          | {'precision': 1.0, 'recall': 0.7368421052631579, 'f1-score': 0.8484848484848484, 'support': 19}                | {'precision': 0.7333333333333333, 'recall': 0.6470588235294118, 'f1-score': 0.6875, 'support': 17}             | {'precision': 0.868421052631579, 'recall': 0.9565217391304348, 'f1-score': 0.9103448275862069, 'support': 69}  | 0.8667   | {'precision': 0.867251461988304, 'recall': 0.7801408893076681, 'f1-score': 0.8154432253570185, 'support': 105}    | {'precision': 0.870359231411863, 'recall': 0.8666666666666667, 'f1-score': 0.863071478330099, 'support': 105}    |
+| 0.4588        | 5.0   | 560  | 0.7815          | {'precision': 0.7727272727272727, 'recall': 0.8947368421052632, 'f1-score': 0.8292682926829269, 'support': 19} | {'precision': 0.6470588235294118, 'recall': 0.6470588235294118, 'f1-score': 0.6470588235294118, 'support': 17} | {'precision': 0.8939393939393939, 'recall': 0.855072463768116, 'f1-score': 0.8740740740740741, 'support': 69}  | 0.8286   | {'precision': 0.7712418300653595, 'recall': 0.7989560431342637, 'f1-score': 0.7834670634288043, 'support': 105}   | {'precision': 0.832034632034632, 'recall': 0.8285714285714286, 'f1-score': 0.8292115111627308, 'support': 105}   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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,
+  "id2label": {
+    "0": "negative",
+    "1": "positive",
+    "2": "no_impact",
+    "3": "mixed"
+  },
+  "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": "4.35.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

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