End of training

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,61 @@
+---
+license: apache-2.0
+base_model: Twitter/twhin-bert-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: disaster_model
+  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. -->
+
+# disaster_model
+
+This model is a fine-tuned version of [Twitter/twhin-bert-base](https://huggingface.co/Twitter/twhin-bert-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5129
+- Accuracy: 0.7866
+
+## 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: 2e-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: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 381  | 0.5129          | 0.7866   |
+| 0.5557        | 2.0   | 762  | 0.5191          | 0.7905   |
+
+
+### Framework versions
+
+- Transformers 4.33.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3

config.json

@@ -0,0 +1,34 @@
+{
+  "_name_or_path": "Twitter/twhin-bert-base",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Not disaster",
+    "1": "Disaster"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Disaster": 1,
+    "Not disaster": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "relative_key",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250002
+}

pytorch_model.bin

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special_tokens_map.json

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+  "unk_token": "<unk>"
+}

tokenizer.json

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tokenizer_config.json

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+  "clean_up_tokenization_spaces": true,
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+  "eos_token": "</s>",
+  "mask_token": {
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+    "content": "<mask>",
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+    "normalized": true,
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+    "single_word": false
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+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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