Update README.md

README.md

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-# Model Card for Model ID
+# Model Card for Hprophetnet-large
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+This model is a fine-tuned version of [bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on Newsroom dataset to generate news headlines. To ask model to generate headliens 
+"Headline: " should be appended to the beginning of the article. 
 
-## Model Details
+## Intended uses & limitations
+You can use this model for headline generation task on English news articles. 
 
-### Model Description
+### Usage
+```python
 
-<!-- Provide a longer summary of what this model is. -->
 
+article = """Two of the OPEC oil cartel’s 11 members, Nigeria and Venezuela, said today that they would voluntarily cut production in response to declining crude oil prices, which have fallen 20 percent from their peak two months ago.
 
+The move, which would take less than 200,000 barrels of oil a day off the market, follows days of mixed signals from some OPEC officials, who have voiced increasing concern about the rapid drop in prices. Nigeria’s oil minister, Edmund Daukoru, who is president of OPEC this year, recently said the price of oil was “very low.”
 
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Nigeria and Venezuela, which have generally been price hawks within the group, said their decision to cut production grew out of an informal deal reached at OPEC’s last meeting, earlier this month, to pare output if prices fell steeply. Some OPEC representatives have grown anxious at the slide in the oil futures markets, where prices for benchmark contracts have fallen from a midsummer high of $77.03 a barrel.
 
-### Model Sources [optional]
+But traders shrugged off the announcement of the production cuts today. On the New York Mercantile Exchange, the most widely watched contract price — light, low-sulfur crude for delivery next month — traded this afternoon at $62.30 a barrel, down 0.7 percent.
 
-<!-- Provide the basic links for the model. -->
+Mr. Daukoru has been in contact with other OPEC ministers to discuss prices, which on Monday briefly slipped below $60 a barrel for the first time in six months. But the Organization of the Petroleum Exporting Countries, as the cartel is formally known, denied any shift in policy.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+“We are not currently concerned,” a delegate from one of OPEC’s Gulf members said. “The prices are currently manageable and fair. We’re not overly alarmed by the prices. It is not a cause for alarm. It’s the market working.”
 
-## Uses
+It is not unusual for oil prices to fall after Labor Day and the conclusion of the summer travel season. Demand tends to slow in the third quarter, and refiners reduce their output for seasonal maintenance; consumption picks up again with the first winter cold in the Western Hemisphere, and prices sometimes do as well.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+“We are not going to push extra oil in the market or force it down our customers’ throats, we just respond to demand,” the delegate from the Gulf said.
 
-### Direct Use
+Still, contradictory statements from senior OPEC representatives have sown doubt about the oil cartel’s strategy. Whether OPEC countries actually reduce their output or not, the mixed messages have at least succeeded in one way: oil traders have been persuaded that OPEC is willing to step in to defend prices, and have traded on that belief, slowing the recent price decline.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+“While apparently fanciful, reports of an imminent output cut reflect two hard facts: stocks are building faster than expected, and several producers have an incredibly low pain threshold when it comes to price drops,” Antoine Halff, an energy analyst with Fimat, wrote in a note to clients today. “However, more price declines will likely be needed before OPEC producers decide on any coordinated move.”
 
-[More Information Needed]
+Venezuela, which pumps about 2.5 million barrels a day, said it would cut its daily output by 50,000 barrels, or about 2 percent, starting Oct. 1. Nigeria said it would trim its exports by 5 percent on the same date, a reduction of about 120,000 barrels a day from its current output of about 3.8 million barrels a day.
 
-### Downstream Use [optional]
+“They are trying to influence the psychology of the market,” said Larry Goldstein, a veteran oil analyst and the president of the Petroleum Industry Research Foundation in New York. “Although they are reacting to the reduction in demand, they are trying to convince the market that they are actually anticipating it, by making cuts ahead of the market. But they are simply reacting to it, which is how markets should operate.”"""
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+import transformers
+import os
+import torch
+os.environ["CUDA_VISIBLE_DEVICES"]="5"
+from transformers import AutoModelWithLMHead,AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer, AutoTokenizer, AutoModelForSequenceClassification
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
 
-[More Information Needed]
+#appending the task identifier to the beginning of input
+article = "Headline: " + article
 
-### Out-of-Scope Use
+model = AutoModelForSeq2SeqLM.from_pretrained("omidvaramin/HBART").to(device)
+tokenizer = AutoTokenizer.from_pretrained("omidvaramin/HBART")
+#encodign article using tokenizer
+encoding = tokenizer(article
+                     , max_length=1024
+                     , truncation=True
+                     ,return_tensors="pt"
+                    ,padding='longest')
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+input_ids = encoding['input_ids']
+attention_masks = encoding['attention_mask']
 
-[More Information Needed]
+#transfering the data into GPU
+input_ids = input_ids.to(device)
+attention_masks = attention_masks.to(device)
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+#generate headlines using kbeam technique
+beam_outputs = model.generate(
+                            input_ids = input_ids,
+                            attention_mask = attention_masks
+                            ,do_sample = False
+                            ,num_beams = 4
+                            ,max_length = 20
+                            ,min_length = 1
+                            ,num_return_sequences = 1
+#                             ,top_k=0
+#                             ,temperature=0.7
+                                )
 
-[More Information Needed]
+result = tokenizer.batch_decode(beam_outputs,
+                                skip_special_tokens=True)
+print(result[0])
 
-### Recommendations
+```
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+### BibTeX entry and citation info
 
+```bibtex
+@ARTICLE{10154027,
+  author={Omidvar, Amin and An, Aijun},
+  journal={IEEE Access}, 
+  title={Learning to Generate Popular Headlines}, 
+  year={2023},
+  volume={11},
+  number={},
+  pages={60904-60914},
+  doi={10.1109/ACCESS.2023.3286853}}