Trained

.gitignore

@@ -0,0 +1,4 @@
+.ipynb_checkpoints/
+checkpoint-*/
+runs/
+

README.md

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+---
+language: ti
+widget:
+- text: "ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር"
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+model-index:
+- name: tielectra-small-sentiment
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.8228962818003914
+    - name: Precision
+      type: precision
+      value: 0.8055555555555556
+    - name: Recall
+      type: recall
+      value: 0.841
+    - name: Accuracy
+      type: accuracy
+      value: 0.819
+---
+
+
+# Sentiment Analysis for Tigrinya with TiELECTRA
+
+This model is a fine-tuned version of [TiELECTRA](https://huggingface.co/fgaim/tielectra-small) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
+
+
+## Training
+
+### Hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Results
+
+The model achieves the following results on the evaluation set:
+- F1: 0.8229
+- Precision: 0.8056
+- Recall: 0.841
+- Accuracy: 0.819
+- Loss: 0.4299
+
+### Framework versions
+
+- Transformers 4.10.3
+- Pytorch 1.9.0+cu111
+- Datasets 1.10.2
+- Tokenizers 0.10.1
+
+
+## Citation
+
+If you use this model in your product or research, please cite as follows:
+
+```
+@article{Fitsum2021TiPLMs,
+  author= {Fitsum Gaim and Wonsuk Yang and Jong C. Park},
+  title= {Monolingual Pre-trained Language Models for Tigrinya},
+  year= 2021,
+  publisher= {WiNLP 2021/EMNLP 2021}
+}
+```
+
+
+## References
+
+```
+Tela, A., Woubie, A. and Hautamäki, V. 2020.
+Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya.
+ArXiv, abs/2006.07698.
+```

all_results.json

@@ -0,0 +1,17 @@
+{
+    "epoch": 3.0,
+    "eval_accuracy": 0.819,
+    "eval_f1": 0.8228962818003914,
+    "eval_loss": 0.42992153763771057,
+    "eval_precision": 0.8055555555555556,
+    "eval_recall": 0.841,
+    "eval_runtime": 4.0127,
+    "eval_samples": 4000,
+    "eval_samples_per_second": 996.834,
+    "eval_steps_per_second": 31.151,
+    "train_loss": 0.3768685761384613,
+    "train_runtime": 506.9261,
+    "train_samples": 49562,
+    "train_samples_per_second": 293.309,
+    "train_steps_per_second": 9.167
+}

config.json

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+{
+  "_name_or_path": "tielectra-small-sentiment",
+  "architectures": [
+    "ElectraForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 128,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "id2label": {
+    "0": "NEGATIVE",
+    "1": "POSITIVE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "label2id": {
+    "NEGATIVE": "0",
+    "POSITIVE": "1"
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.10.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

eval_results.json

@@ -0,0 +1,12 @@
+{
+    "epoch": 3.0,
+    "eval_accuracy": 0.819,
+    "eval_f1": 0.8228962818003914,
+    "eval_loss": 0.42992153763771057,
+    "eval_precision": 0.8055555555555556,
+    "eval_recall": 0.841,
+    "eval_runtime": 4.0127,
+    "eval_samples": 4000,
+    "eval_samples_per_second": 996.834,
+    "eval_steps_per_second": 31.151
+}

predict_results.txt

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