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Financial News Sent.json

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+{
+  "_name_or_path": "distilroberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
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+  "hidden_act": "gelu",
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+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
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+}

README.md

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+---
+license: apache-2.0
+thumbnail: https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis/resolve/main/logo_no_bg.png
+tags:
+- generated_from_trainer
+- financial
+- stocks
+- sentiment
+widget:
+- text: "Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004 ."
+- text: "Dunder mifflin Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004 ."
+datasets:
+- financial_phrasebank
+metrics:
+- accuracy
+model-index:
+- name: distilRoberta-financial-sentiment
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: financial_phrasebank
+      type: financial_phrasebank
+      args: sentences_allagree
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9923008849557522
+---
+<!-- 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. -->
+
+
+<div style="text-align:center;width:250px;height:250px;">
+    <img src="https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis/resolve/main/logo_no_bg.png" alt="logo">
+</div>
+
+# DistilRoberta-financial-sentiment-v2
+
+
+This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the financial_phrasebank dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1116
+- Accuracy: **0.99**23
+## Base Model description
+This model is a distilled version of the [RoBERTa-base model](https://huggingface.co/roberta-base). It follows the same training procedure as [DistilBERT](https://huggingface.co/distilbert-base-uncased).
+The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/distillation).
+This model is case-sensitive: it makes a difference between English and English.
+The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base).
+On average DistilRoBERTa is twice as fast as Roberta-base.
+## Training Data
+Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by agreement rate of 5-8 annotators.
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- 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
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 255  | 0.1670          | 0.9646   |
+| 0.209         | 2.0   | 510  | 0.2290          | 0.9558   |
+| 0.209         | 3.0   | 765  | 0.2044          | 0.9558   |
+| 0.0326        | 4.0   | 1020 | 0.1116          | 0.9823   |
+| 0.0326        | 5.0   | 1275 | 0.1127          | 0.9779   |
+### Framework versions
+- Transformers 4.10.2
+- Pytorch 1.9.0+cu102
+- Datasets 1.12.1
+- Tokenizers 0.10.3

Special Tokens.json

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config.json

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+  ],
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generation_config.json

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special_tokens_map.json

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tokenizer_config.json

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