rv2307
/

keyphrase-abstraction-t5-small

@@ -21,183 +21,78 @@ This modelcard aims to be a base template for new models. It has been generated
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+- **Developed by:** **விபின்**
+- **Model type:** T5-small
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0 license
+- **Finetuned from model [optional]:** T5-small model
 ## Uses
+This model aims to respond with extractive and abstractive keyphrases for the given content. Kindly use "find keyphrase: " as the task prefix prompt to get the desired outputs.
 ## Bias, Risks, and Limitations
+This model response is based on the inputs given to it. So if any Harmful sentences given to this model, it will respond according to that.
 ## How to Get Started with the Model
+```
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
+model_dir = "rv2307/keyphrase-abstraction-t5-small"
+tokenizer = T5Tokenizer.from_pretrained(model_dir)
+model = T5ForConditionalGeneration.from_pretrained(model_dir, torch_dtype=torch.bfloat16)
+device = "cuda"
+model.to(device)
+def generate(text):
+    text = "find keyphrase: "  + text
+    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
+    inputs = {k:v.to(model.device) for k,v in inputs.items()}
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs['input_ids'],
+            attention_mask=inputs['attention_mask'],
+            max_length=100,
+            use_cache=True
+        )
+    output_list = tokenizer.decode(outputs[0],skip_special_tokens=True)
+    return output_list
+content = "Hi, How are you??"
+outputs = generate(content)
+print(outputs)
+```
 ## Training Details
 ### Training Data
+Mostly used open source datasets for these tasks, which are already available on the huggingface.
 ### Training Procedure
+This model has been fine tuned for 6 epochs with 40k datasets collected from the internet.
 ### Results
+```
+Epoch	Training Loss	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
+1	0.105800	0.087497	43.840900	19.029900	40.303200	40.320300	16.306200
+2	0.097600	0.081029	46.335000	21.246800	42.377400	42.387500	16.404900
+3	0.091800	0.077546	47.721200	22.467200	43.622400	43.632000	16.308200
+4	0.087600	0.075441	48.633700	23.351300	44.493800	44.504300	16.359000
+5	0.088200	0.074088	48.977500	23.747000	44.804900	44.813200	16.300500
+6	0.084900	0.073381	49.347300	24.029500	45.097100	45.108300	16.332600
+```