Update README.md

README.md

@@ -27,26 +27,34 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## Uses
 
+```python
+import json
+import torch
+from transformers import AutoTokenizer
+from transformers import AutoModelForCausalLM
+from datetime import datetime
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
+model_id = "Sefika/semeval_base_1"
+model = T5ForConditionalGeneration.from_pretrained(model_id,
+                                                    device_map="auto",
+                                                    load_in_8bit=False,
+                                                    torch_dtype=torch.float16)
+prompt = """Example Sentence:The purpose of the <e1>audit</e1> was to report on the <e2>financial statements</e2>.\n"""+\
+         """Sentence: Query Sentence:The most common <e1>audits</e1> were about <e2>waste</e2> and recycling.\n"""+\
+         """What is the relation type between e1: audits. and e2 : waste.  according to given relation types below in the sentence?\n"""+\
+         """Relation types: Relation types: Cause-Effect(e2,e1), Content-Container(e1,e2), Member-Collection(e1,e2), Instrument-Agency(e1,e2), Product-Producer(e2,e1), Member-Collection(e2,e1), Message-Topic(e1,e2), Entity-Origin(e2,e1), Message-Topic(e2,e1), Instrument-Agency(e2,e1), Content-Container(e2,e1), Product-Producer(e1,e2), Entity-Origin(e1,e2), Component-Whole(e1,e2), Entity-Destination(e1,e2), Other, Cause-Effect(e1,e2), Component-Whole(e2,e1), Entity-Destination(e2,e1). \n"""
+inputs = self.tokenizer(prompt, add_special_tokens=True, max_length=526,return_tensors="pt").input_ids.to("cuda")
+
+outputs = self.model.generate(inputs, max_new_tokens=length,  pad_token_id=self.tokenizer.eos_token_id)
+
+response = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
+print(response[0])
+#"Cause-Effect(e1,e2)"
+
+```
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
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-### 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
 
@@ -58,65 +66,16 @@ semeval-2010-task8
 ### Training Procedure
 
 5 fold cross validation with sentence and relation types. Input is sentence and the output is relation types
-#### Preprocessing [optional]
-
-[More Information Needed]
-
 
 #### Training Hyperparameters
 
 Epoch:5, BS:16 and others are default.
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
-
-[More Information Needed]
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-#### Summary
-
-
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-## 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 -->
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
 
 #### Hardware
 
 Colab Pro+ A100.
 
-#### Software
 
-[More Information Needed]
 
 ## Citation [optional]