upload fp32 model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard 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

@@ -1,3 +1,77 @@
 ---
+language:
+- en
 license: mit
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+- f1
+model-index:
+- name: xlm-roberta-base-mrpc
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE MRPC
+      type: glue
+      args: mrpc
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8578431372549019
+    - name: F1
+      type: f1
+      value: 0.901023890784983
 ---
+
+<!-- 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. -->
+
+# xlm-roberta-base-mrpc
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the GLUE MRPC dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3703
+- Accuracy: 0.8578
+- F1: 0.9010
+- Combined Score: 0.8794
+
+## 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: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.10.0+cu102
+- Datasets 2.1.0
+- Tokenizers 0.11.6

all_results.json

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+    "eval_combined_score": 0.8794335140199425,
+    "eval_f1": 0.901023890784983,
+    "eval_loss": 0.3703046441078186,
+    "eval_runtime": 14.8109,
+    "eval_samples": 408,
+    "eval_samples_per_second": 27.547,
+    "eval_steps_per_second": 3.443,
+    "train_loss": 0.4503614044189453,
+    "train_runtime": 4427.9443,
+    "train_samples": 3668,
+    "train_samples_per_second": 4.142,
+    "train_steps_per_second": 0.26
+}

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,
+  "finetuning_task": "mrpc",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "not_equivalent",
+    "1": "equivalent"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "equivalent": 1,
+    "not_equivalent": 0
+  },
+  "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": "4.18.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8578431372549019,
+    "eval_combined_score": 0.8794335140199425,
+    "eval_f1": 0.901023890784983,
+    "eval_loss": 0.3703046441078186,
+    "eval_runtime": 14.8109,
+    "eval_samples": 408,
+    "eval_samples_per_second": 27.547,
+    "eval_steps_per_second": 3.443
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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train_results.json

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+    "train_samples_per_second": 4.142,
+    "train_steps_per_second": 0.26
+}

trainer_state.json

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+}

training_args.bin

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