Initial Commit

README.md

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+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- tweet_sentiment_multilingual
+metrics:
+- accuracy
+- f1
+model-index:
+- name: scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: tweet_sentiment_multilingual
+      type: tweet_sentiment_multilingual
+      config: all
+      split: validation
+      args: all
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6180555555555556
+    - name: F1
+      type: f1
+      value: 0.616361106308878
+---
+
+<!-- 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. -->
+
+# scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6171
+- Accuracy: 0.6181
+- F1: 0.6164
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 1234
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.9992        | 1.09  | 500  | 0.9022          | 0.5895   | 0.5790 |
+| 0.8353        | 2.17  | 1000 | 0.8484          | 0.6169   | 0.6146 |
+| 0.7029        | 3.26  | 1500 | 0.9391          | 0.6312   | 0.6286 |
+| 0.5653        | 4.35  | 2000 | 1.0629          | 0.6157   | 0.6032 |
+| 0.4622        | 5.43  | 2500 | 1.1849          | 0.6169   | 0.6091 |
+| 0.3614        | 6.52  | 3000 | 1.2831          | 0.6184   | 0.6190 |
+| 0.284         | 7.61  | 3500 | 1.3589          | 0.6177   | 0.6196 |
+| 0.2384        | 8.7   | 4000 | 1.6171          | 0.6181   | 0.6164 |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.1.1+cu121
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "xlm-roberta-base",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "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": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

pytorch_model.bin

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training_args.bin

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