Duplicate from soleimanian/financial-roberta-large-sentiment

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Co-authored-by: Mohammad Soleimanian <soleimanian@users.noreply.huggingface.co>

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README.md +51 -0
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README.md ADDED Viewed

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+---
+license: apache-2.0
+language:
+  - eng
+tags:
+- text-classification
+- Sentiment
+- RoBERTa
+- Financial Statements
+- Accounting
+- Finance
+- Business
+- ESG
+- CSR Reports
+- Financial News
+- Earnings Call Transcripts
+- Sustainability
+- Corporate governance
+---
+<!DOCTYPE html>
+<html>
+<body>
+<h1><b>Financial-RoBERTa</b></h1>
+<p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p>
+<ul style="PADDING-LEFT: 40px">
+  <li>Financial Statements,</li>
+  <li>Earnings Announcements,</li>
+  <li>Earnings Call Transcripts,</li>
+  <li>Corporate Social Responsibility (CSR) Reports,</li>
+  <li>Environmental, Social, and Governance (ESG) News,</li>
+  <li>Financial News,</li>
+  <li>Etc.</li>
+</ul>
+<p>Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text.</p>
+<p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p>
+<p><b>How to perform sentiment analysis:</b></p>
+<p>The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:</p>
+<pre>
+  <code>
+from transformers import pipeline
+sentiment_analysis = pipeline("sentiment-analysis",model="soleimanian/financial-roberta-large-sentiment")
+print(sentiment_analysis("In fiscal 2021, we generated a net yield of approximately 4.19% on our investments, compared to approximately 5.10% in fiscal 2020."))
+  </code>
+</pre>
+<p>I provide an example script via <a href="https://colab.research.google.com/drive/11RGWU3UDtxnjan8Ug6dyX82m9fBV6CGo?usp=sharing" target="_blank">Google Colab</a>. You can load your data to a Google Drive and run the script for free on a Colab.
+<p><b>Citation and contact:</b></p>
+<p>Please cite <a href="#" target="_blank">this paper</a> when you use the model. Feel free to reach out to mohammad.soleimanian@concordia.ca with any questions or feedback you may have.<p/>
+</body>
+</html>

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+{
+  "_name_or_path": "/home/soleiman/scratch/train/roberta-large-output",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "negative",
+    "1": "neutral",
+    "2": "positive"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "negative": 0,
+    "neutral": 1,
+    "positive": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.12.0.dev0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

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