Upload 11 files

README.md

@@ -0,0 +1,69 @@
+---
+language: id
+tags:
+  - indonesian-roberta-base-indolem-sentiment-classifier-fold-0
+license: mit
+datasets:
+  - indolem
+widget:
+  - text: "Pelayanan hotel ini sangat baik."
+---
+
+## Indonesian RoBERTa Base IndoLEM Sentiment Classifier
+
+Indonesian RoBERTa Base IndoLEM Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then fine-tuned on [`indolem`](https://indolem.github.io/)'s [Sentiment Analysis](https://github.com/indolem/indolem/tree/main/sentiment) dataset consisting of Indonesian tweets and hotel reviews (Koto et al., 2020).
+
+A 5-fold cross-validation experiment was performed, with splits provided by the original dataset authors. This model was trained on fold 0. You can find models trained on [fold 0](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0), [fold 1](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-1), [fold 2](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-2), [fold 3](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-3), and [fold 4](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-4), in their respective links.
+
+On **fold 0**, the model achieved an F1 of 86.42% on dev/validation and 83.12% on test. On all **5 folds**, the models achieved an average F1 of 84.14% on dev/validation and 84.64% on test.
+
+Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
+
+## Model
+
+| Model                                                         | #params | Arch.        | Training/Validation data (text) |
+| ------------------------------------------------------------- | ------- | ------------ | ------------------------------- |
+| `indonesian-roberta-base-indolem-sentiment-classifier-fold-0` | 124M    | RoBERTa Base | `IndoLEM`'s Sentiment Analysis  |
+
+## Evaluation Results
+
+The model was trained for 10 epochs and the best model was loaded at the end.
+
+| Epoch | Training Loss | Validation Loss | Accuracy | F1       | Precision | Recall   |
+| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
+| 1     | 0.563500      | 0.420457        | 0.796992 | 0.626728 | 0.680000  | 0.581197 |
+| 2     | 0.293600      | 0.281360        | 0.884712 | 0.811475 | 0.779528  | 0.846154 |
+| 3     | 0.163000      | 0.267922        | 0.904762 | 0.844262 | 0.811024  | 0.880342 |
+| 4     | 0.090200      | 0.335411        | 0.899749 | 0.838710 | 0.793893  | 0.888889 |
+| 5     | 0.065200      | 0.462526        | 0.897243 | 0.835341 | 0.787879  | 0.888889 |
+| 6     | 0.039200      | 0.423001        | 0.912281 | 0.859438 | 0.810606  | 0.914530 |
+| 7     | 0.025300      | 0.452100        | 0.912281 | 0.859438 | 0.810606  | 0.914530 |
+| 8     | 0.010400      | 0.525200        | 0.914787 | 0.855932 | 0.848739  | 0.863248 |
+| 9     | 0.007100      | 0.513585        | 0.909774 | 0.850000 | 0.829268  | 0.871795 |
+| 10    | 0.007200      | 0.537254        | 0.917293 | 0.864198 | 0.833333  | 0.897436 |
+
+## How to Use
+
+### As Text Classifier
+
+```python
+from transformers import pipeline
+
+pretrained_name = "w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0"
+
+nlp = pipeline(
+    "sentiment-analysis",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+nlp("Pelayanan hotel ini sangat baik.")
+```
+
+## Disclaimer
+
+Do consider the biases which come from both the pre-trained RoBERTa model and `IndoLEM`'s Sentiment Analysis dataset that may be carried over into the results of this model.
+
+## Author
+
+Indonesian RoBERTa Base IndoLEM Sentiment Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "flax-community/indonesian-roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.11.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

gitattributes

@@ -0,0 +1,28 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text 
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff436956548e288d57a2f7e2c8191adfdc7a717cb42068d5fea3336974ad0be3
+size 498617024

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3aead81fbdb4611873df5ec497ac2f853236d2743e3192b6c302440625bdd87c
+size 498674093

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

tokenizer_config.json

@@ -0,0 +1 @@
+{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "special_tokens_map_file": null, "name_or_path": "flax-community/indonesian-roberta-base", "tokenizer_class": "RobertaTokenizer"}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0faa92bd7ed077549742faac2919a8b42ffcfcc5b5defc66435cdacb4c7b56ef
+size 2799