Initial Commit

README.md

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+---
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+datasets:
+- tweet_sentiment_multilingual
+metrics:
+- accuracy
+- f1
+model-index:
+- name: scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all
+  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: 0.6439043209876543
+    - name: F1
+      type: f1
+      value: 0.6438207626180543
+---
+
+<!-- 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-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all
+
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tweet_sentiment_multilingual dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.0654
+- Accuracy: 0.6439
+- F1: 0.6438
+
+## 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: 64
+- seed: 66
+- 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.8887        | 1.09  | 500  | 0.9177          | 0.5883   | 0.5662 |
+| 0.6752        | 2.17  | 1000 | 0.9192          | 0.6555   | 0.6536 |
+| 0.5105        | 3.26  | 1500 | 0.9600          | 0.6535   | 0.6491 |
+| 0.3805        | 4.35  | 2000 | 1.0619          | 0.6566   | 0.6542 |
+| 0.2836        | 5.43  | 2500 | 1.3402          | 0.6505   | 0.6508 |
+| 0.24          | 6.52  | 3000 | 1.4206          | 0.6617   | 0.6613 |
+| 0.2011        | 7.61  | 3500 | 1.4950          | 0.6377   | 0.6387 |
+| 0.1626        | 8.7   | 4000 | 1.7447          | 0.6470   | 0.6421 |
+| 0.141         | 9.78  | 4500 | 1.7487          | 0.6443   | 0.6436 |
+| 0.1187        | 10.87 | 5000 | 1.7271          | 0.6420   | 0.6416 |
+| 0.0999        | 11.96 | 5500 | 2.0654          | 0.6439   | 0.6438 |
+
+
+### 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": "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": "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-07,
+  "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": "4.33.3",
+  "type_vocab_size": 0,
+  "vocab_size": 251000
+}

pytorch_model.bin

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training_args.bin

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