Upload GoEmotions RoBERTa-large focal SOTA model
Browse files- README.md +98 -0
- config.json +89 -0
- kaggle-run.log +0 -0
- labels.json +33 -0
- metrics.json +2209 -0
- model.safetensors +3 -0
- thresholds.json +127 -0
- tokenizer.json +0 -0
- tokenizer_config.json +16 -0
- training_args.bin +3 -0
README.md
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---
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license: apache-2.0
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base_model: FacebookAI/roberta-large
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datasets:
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- google-research-datasets/go_emotions
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language:
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- en
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tags:
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- goemotions
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- emotion-classification
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- multi-label-classification
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- roberta
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- focal-loss
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- threshold-optimization
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metrics:
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- f1
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pipeline_tag: text-classification
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library_name: transformers
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---
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# GoEmotions RoBERTa Large Focal SOTA
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RoBERTa-large multi-label classifier fine-tuned on the public GoEmotions
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`simplified` split. The model predicts 28 labels: 27 fine-grained emotions plus
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`neutral`.
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## Results
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The selected policy is coordinate threshold search optimized on validation
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macro-F1.
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| Split | Macro F1 | Micro F1 | Samples F1 | Hamming Loss |
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| --- | ---: | ---: | ---: | ---: |
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| Validation | 0.565864 | 0.596637 | 0.605125 | 0.034424 |
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| Test | 0.533020 | 0.576651 | 0.585942 | 0.035820 |
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The per-label threshold candidate reached test macro-F1 `0.534994`, but the
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validation-selected policy is the coordinate threshold policy above.
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## Training
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- Base model: `FacebookAI/roberta-large`
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- Dataset: `google-research-datasets/go_emotions`, `simplified`
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- Loss: focal loss, alpha `0.38`, gamma `2.8`
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- Epochs: `4`
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- Learning rate: `1e-5`
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- Batch: `2`, gradient accumulation `16`
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- Mixed precision: disabled for stability on Kaggle T4
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- Seed: `42`
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Training provenance: Kaggle kernel
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`kevin250304/goemotions-roberta-large-focal-sweep`.
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## Usage
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```python
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import json
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import numpy as np
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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repo_id = "AliceYin/goemotions-roberta-large-focal-sota"
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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model = AutoModelForSequenceClassification.from_pretrained(repo_id)
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labels = json.load(open("labels.json"))["label_names"]
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thresholds = json.load(open("thresholds.json"))["coordinate"]
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threshold_array = np.array([thresholds[label] for label in labels])
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text = "I can't believe this worked so well."
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
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with torch.no_grad():
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probs = torch.sigmoid(model(**inputs).logits).cpu().numpy()[0]
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predicted = [
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label for label, prob, threshold in zip(labels, probs, threshold_array)
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if prob >= threshold
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]
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print(predicted)
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```
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For remote loading of thresholds and labels, download `labels.json` and
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`thresholds.json` from this repository alongside the model files.
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## Files
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- `model.safetensors`: fine-tuned RoBERTa-large weights
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- `config.json`, `tokenizer.json`, `tokenizer_config.json`: Transformers files
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- `thresholds.json`: fixed, global, per-label, and coordinate thresholds
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- `labels.json`: label names and neutral index
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- `metrics.json`: full validation/test metrics
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- `kaggle-run.log`: training log
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## Limitations
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This model is trained on Reddit comments and inherits the dataset's domain,
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annotation, and class imbalance limitations. Validate before using in
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high-stakes settings.
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config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"RobertaForSequenceClassification"
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],
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| 6 |
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"attention_probs_dropout_prob": 0.1,
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| 7 |
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"bos_token_id": 0,
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| 8 |
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"classifier_dropout": null,
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| 9 |
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"dtype": "float32",
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| 10 |
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"eos_token_id": 2,
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| 11 |
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"hidden_act": "gelu",
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| 12 |
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"hidden_dropout_prob": 0.1,
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| 13 |
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"hidden_size": 1024,
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| 14 |
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"id2label": {
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| 15 |
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"0": "admiration",
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"1": "amusement",
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"2": "anger",
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"3": "annoyance",
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"4": "approval",
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"5": "caring",
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"6": "confusion",
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"7": "curiosity",
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"8": "desire",
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"9": "disappointment",
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"10": "disapproval",
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"11": "disgust",
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| 27 |
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"12": "embarrassment",
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| 28 |
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"13": "excitement",
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| 29 |
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"14": "fear",
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| 30 |
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"15": "gratitude",
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| 31 |
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"16": "grief",
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| 32 |
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"17": "joy",
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| 33 |
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"18": "love",
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| 34 |
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"19": "nervousness",
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| 35 |
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"20": "optimism",
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"21": "pride",
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| 37 |
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"22": "realization",
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| 38 |
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"23": "relief",
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| 39 |
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"24": "remorse",
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| 40 |
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"25": "sadness",
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"26": "surprise",
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"27": "neutral"
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},
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| 44 |
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"initializer_range": 0.02,
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| 45 |
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"intermediate_size": 4096,
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| 46 |
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"is_decoder": false,
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| 47 |
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"label2id": {
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| 48 |
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"admiration": 0,
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| 49 |
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"amusement": 1,
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| 50 |
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"anger": 2,
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| 51 |
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"annoyance": 3,
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| 52 |
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"approval": 4,
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| 53 |
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"caring": 5,
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| 54 |
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"confusion": 6,
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| 55 |
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"curiosity": 7,
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| 56 |
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"desire": 8,
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| 57 |
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"disappointment": 9,
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| 58 |
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"disapproval": 10,
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| 59 |
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"disgust": 11,
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| 60 |
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"embarrassment": 12,
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| 61 |
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"excitement": 13,
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| 62 |
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"fear": 14,
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| 63 |
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"gratitude": 15,
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| 64 |
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"grief": 16,
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| 65 |
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"joy": 17,
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| 66 |
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"love": 18,
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| 67 |
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"nervousness": 19,
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| 68 |
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"neutral": 27,
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| 69 |
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"optimism": 20,
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| 70 |
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"pride": 21,
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| 71 |
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"realization": 22,
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| 72 |
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"relief": 23,
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| 73 |
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"remorse": 24,
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| 74 |
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"sadness": 25,
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| 75 |
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"surprise": 26
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| 76 |
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},
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| 77 |
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"layer_norm_eps": 1e-05,
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| 78 |
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"max_position_embeddings": 514,
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| 79 |
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"model_type": "roberta",
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| 80 |
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"num_attention_heads": 16,
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| 81 |
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"num_hidden_layers": 24,
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| 82 |
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"pad_token_id": 1,
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| 83 |
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"problem_type": "multi_label_classification",
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| 84 |
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"tie_word_embeddings": true,
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| 85 |
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"transformers_version": "5.0.0",
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| 86 |
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"type_vocab_size": 1,
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| 87 |
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"use_cache": false,
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| 88 |
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"vocab_size": 50265
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}
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kaggle-run.log
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The diff for this file is too large to render.
See raw diff
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labels.json
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{
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| 2 |
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"label_names": [
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"admiration",
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| 4 |
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"amusement",
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| 5 |
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"anger",
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| 6 |
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"annoyance",
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| 7 |
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"approval",
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| 8 |
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"caring",
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| 9 |
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"confusion",
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| 10 |
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"curiosity",
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| 11 |
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"desire",
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| 12 |
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"disappointment",
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| 13 |
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"disapproval",
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| 14 |
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"disgust",
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| 15 |
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"embarrassment",
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| 16 |
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"excitement",
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| 17 |
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"fear",
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| 18 |
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"gratitude",
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| 19 |
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"grief",
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| 20 |
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"joy",
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| 21 |
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"love",
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| 22 |
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"nervousness",
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| 23 |
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"optimism",
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| 24 |
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"pride",
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| 25 |
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"realization",
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| 26 |
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"relief",
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| 27 |
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"remorse",
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| 28 |
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"sadness",
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| 29 |
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"surprise",
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| 30 |
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"neutral"
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| 31 |
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],
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| 32 |
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"neutral_index": 27
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| 33 |
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}
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metrics.json
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thresholds.json
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tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
ADDED
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@@ -0,0 +1,16 @@
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training_args.bin
ADDED
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