Datasets:

NOSIBLE
/

financial-sentiment

@@ -1,98 +1,106 @@
----
-license: odc-by
----
-# Nosible Financial Sentiment Dataset
-## What is it?
-The nosible financial sentiment dataset is a collection of **100k** cleaned and deduplicated financial news samples for financial sentiment classification. Each sample is labeled as **positive**, **negative**, or **neutral**. Financial sentiment is optimised for used in finance as it's sensitive to financial outlook rather than just emotional tone.
-## How to use it
-Using the [HuggingFace datasets library](https://huggingface.co/docs/datasets/):
-Tip: You can install it with `pip install datasets`.
-```python
-from datasets import load_dataset
-dataset = load_dataset("NOSIBLE/financial-sentiment")
-print(dataset)
-# DatasetDict({
-#     train: Dataset({
-#         features: ['text', 'label', 'netloc', 'url'],
-#         num_rows: 100000
-#     })
-# })
-# What's next?
-#     Train your model 🤖
-#     Profit 💰
-```
-## Changelog
-- **v1.0.0:** Initial version
-## Dataset Structure
-### Data Instances
-The following is an example sample from the dataset:
-```json
-{
-    "text": "Offshore staff HOUSTON \u2013 VAALCO Energy is looking to bring in a floating storage and offloading (FSO) unit at the Etame Marin oil field offshore Gabon. The company has signed a non-binding letter of intent with Omni Offshore Terminals to supply and operate the vessel at Etame for up to 11 years, following the expiry of the current FPSO Petr\u00f3leo Nautipa contract with BW Offshore in September 2022. Omni has provided a preliminary proposal for leasing and operating the FSO, which could reduce VAALCO's operating costs by 15-25%, compared with the current FPSO contract during the term of the proposed agreement. Maintaining the current FPSO beyond its contract or transitioning to a different FPSO, VAALCO added, would require substantial capex investments.",
-    "label": "positive",
-    "netloc": "www.offshore-mag.com",
-    "url": "https://www.offshore-mag.com/rigs-vessels/article/14202267/vaalco-contemplating-switch-to-fso-at-etame-offshore-gabon"
-}
-```
-### Data Fields
-- `text` (string): A text chunk from a document.
-- `label` (string): The financial sentiment label of the text, sourced from LLMs and refined with active learning (an iterative relabeling process).
-- `netloc` (string): The network location (domain) of the document.
-- `url` (string): The URL of the document.
-## Dataset creation
-### Data source
-The dataset was sampled from the NOSIBLE datafeeds product which provides web scale surveillance data to customers. NOSIBLE datafeeds are results for specific search queries and results are safe because they are sourced from search results from the NOSIBLE search engine, from which data is sourced exclusively from the public web.
-### Relabeling algorithm
-The dataset's label field was annotated from LLMs and refined using an active learning algorithm called relabeling.
-The algorithm outline is as follows:
-1. Hand label a candidate set of ~200 samples to use as a test bed to refine the prompt used by the LLM labelers to classify the text.
-2. Label a set of 10k samples with LLM labelers:
-    - x-ai/grok-4-fast
-    - x-ai/grok-4-fast:thinking
-    - google/gemini-2.5-flash
-    - openai/gpt-5-nano
-    - openai/gpt-4.1-mini
-    - openai/gpt-oss-120b
-    - meta-llama/llama-4-maverick
-    - qwen/qwen3-32b
-3. Train a linear model on the labels using the majority vote of the LLM labelers.
-4. Iterative relabeling (active learning steps) to improve the label quality:
-    - Evaluate linear model's predictions over the samples.
-    - Find disagreements: samples where the LLM labelers agree on a label, but the model has predicted a different label.
-    - Consult a much larger LLM, the oracle, to evaluate the model's prediction and relabel the sample if it agrees with the LLM labels.
-    - Drop the worst performing LLM labeler's from the ensemble.
-    - Repeat the process with the remaining LLM labelers until the number of samples relabeled reaches 0.
-    - Store the refined relabeled dataset.
-5. This is the final dataset used for training the NOSIBLE/financial-sentiment-v1.1-base model, which is a finetune.
-## Additional information
-## License
-The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/).
-## Attribution
-- [NOSIBLE Inc](https://www.nosible.ai/) Team
-## Citations
-Coming soon, we're working on a white paper.

+---
+license: odc-by
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- finance
+size_categories:
+- 100K<n<1M
+---
+# Nosible Financial Sentiment Dataset
+## What is it?
+The nosible financial sentiment dataset is a collection of **100k** cleaned and deduplicated financial news samples for financial sentiment classification. Each sample is labeled as **positive**, **negative**, or **neutral**. Financial sentiment is optimised for used in finance as it's sensitive to financial outlook rather than just emotional tone.
+## How to use it
+Using the [HuggingFace datasets library](https://huggingface.co/docs/datasets/):
+Tip: You can install it with `pip install datasets`.
+```python
+from datasets import load_dataset
+dataset = load_dataset("NOSIBLE/financial-sentiment")
+print(dataset)
+# DatasetDict({
+#     train: Dataset({
+#         features: ['text', 'label', 'netloc', 'url'],
+#         num_rows: 100000
+#     })
+# })
+# What's next?
+#     Train your model 🤖
+#     Profit 💰
+```
+## Changelog
+- **v1.0.0:** Initial version
+## Dataset Structure
+### Data Instances
+The following is an example sample from the dataset:
+```json
+{
+    "text": "Offshore staff HOUSTON \u2013 VAALCO Energy is looking to bring in a floating storage and offloading (FSO) unit at the Etame Marin oil field offshore Gabon. The company has signed a non-binding letter of intent with Omni Offshore Terminals to supply and operate the vessel at Etame for up to 11 years, following the expiry of the current FPSO Petr\u00f3leo Nautipa contract with BW Offshore in September 2022. Omni has provided a preliminary proposal for leasing and operating the FSO, which could reduce VAALCO's operating costs by 15-25%, compared with the current FPSO contract during the term of the proposed agreement. Maintaining the current FPSO beyond its contract or transitioning to a different FPSO, VAALCO added, would require substantial capex investments.",
+    "label": "positive",
+    "netloc": "www.offshore-mag.com",
+    "url": "https://www.offshore-mag.com/rigs-vessels/article/14202267/vaalco-contemplating-switch-to-fso-at-etame-offshore-gabon"
+}
+```
+### Data Fields
+- `text` (string): A text chunk from a document.
+- `label` (string): The financial sentiment label of the text, sourced from LLMs and refined with active learning (an iterative relabeling process).
+- `netloc` (string): The network location (domain) of the document.
+- `url` (string): The URL of the document.
+## Dataset creation
+### Data source
+The dataset was sampled from the NOSIBLE datafeeds product which provides web scale surveillance data to customers. NOSIBLE datafeeds are results for specific search queries and results are safe because they are sourced from search results from the NOSIBLE search engine, from which data is sourced exclusively from the public web.
+### Relabeling algorithm
+The dataset's label field was annotated from LLMs and refined using an active learning algorithm called relabeling.
+The algorithm outline is as follows:
+1. Hand label a candidate set of ~200 samples to use as a test bed to refine the prompt used by the LLM labelers to classify the text.
+2. Label a set of 10k samples with LLM labelers:
+    - x-ai/grok-4-fast
+    - x-ai/grok-4-fast:thinking
+    - google/gemini-2.5-flash
+    - openai/gpt-5-nano
+    - openai/gpt-4.1-mini
+    - openai/gpt-oss-120b
+    - meta-llama/llama-4-maverick
+    - qwen/qwen3-32b
+3. Train a linear model on the labels using the majority vote of the LLM labelers.
+4. Iterative relabeling (active learning steps) to improve the label quality:
+    - Evaluate linear model's predictions over the samples.
+    - Find disagreements: samples where the LLM labelers agree on a label, but the model has predicted a different label.
+    - Consult a much larger LLM, the oracle, to evaluate the model's prediction and relabel the sample if it agrees with the LLM labels.
+    - Drop the worst performing LLM labeler's from the ensemble.
+    - Repeat the process with the remaining LLM labelers until the number of samples relabeled reaches 0.
+    - Store the refined relabeled dataset.
+5. This is the final dataset used for training the NOSIBLE/financial-sentiment-v1.1-base model, which is a finetune.
+## Additional information
+## License
+The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/).
+## Attribution
+- [NOSIBLE Inc](https://www.nosible.ai/) Team
+## Citations
+Coming soon, we're working on a white paper.