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-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: 0.6361882716049383
+    - name: F1
+      type: f1
+      value: 0.6387023843949189
+---
+
+<!-- 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
+
+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: 2.0269
+- Accuracy: 0.6362
+- F1: 0.6387
+
+## 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: 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.8991        | 1.09  | 500  | 0.8258          | 0.6265   | 0.6117 |
+| 0.6873        | 2.17  | 1000 | 0.8627          | 0.6481   | 0.6502 |
+| 0.5102        | 3.26  | 1500 | 0.9726          | 0.6516   | 0.6440 |
+| 0.3825        | 4.35  | 2000 | 1.1881          | 0.6578   | 0.6540 |
+| 0.2946        | 5.43  | 2500 | 1.2475          | 0.6532   | 0.6554 |
+| 0.228         | 6.52  | 3000 | 1.5294          | 0.6435   | 0.6458 |
+| 0.2006        | 7.61  | 3500 | 1.6058          | 0.6389   | 0.6342 |
+| 0.159         | 8.7   | 4000 | 1.5956          | 0.6528   | 0.6510 |
+| 0.1334        | 9.78  | 4500 | 1.8463          | 0.6478   | 0.6409 |
+| 0.1052        | 10.87 | 5000 | 2.0269          | 0.6362   | 0.6387 |
+
+
+### 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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