adding better examples to the model card

README.md

@@ -1,11 +1,18 @@
 ---
 license: mit
 
+datasets:
+  - damlab/HIV_FLT
+metrics:
+  - accuracy
+
 widget:
  - text: 'C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C'
- - text: 'C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C' 
- - text: 'C T R P N N N T R R S I R I G P G Q A F Y A T G D I I G D I R Q A H C'
- - text: 'C G R P N N H R I K G L R I G P G R A F F A M G A I G G G E I R Q A H C'
+   example_title: 'V3'
+ - text: 'M E P V D P R L E P W K H P G S Q P K T A C T N C Y C K K C C F H C Q V C F I T K A L G I S Y G R K K R R Q R R R A H Q N S Q T H Q A S L S K Q P T S Q P R G D P T G P K E S K K K V E R E T E T D P F D' 
+   example_title: 'Tat'
+ - text: 'P Q I T L W Q R P L V T I K I G G Q L K E A L L D T G A D D T V L E E M N L P G R W K P K M I G G I G G F I K V R Q Y D Q I L I E I C G H K A I G T V L V G P T P V N I I G R N L L T Q I G C T L N F'
+   example_title: 'PR'
 
 ---
 
@@ -31,13 +38,58 @@ The HIV-BERT model was trained as a refinement of the [ProtBert-BFD model](https
 
 Like the original [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd), this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.
 
+
+
+
 ## Intended Uses & Limitations
 
 As a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.
 
 ## How to use
 
-[Code snippet of AutoModelForMaskedLM prediction of V3 amino acids.]
+As this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position.
+
+```python
+from transformers import pipeline
+
+unmasker = pipeline("fill-mask", model="damlab/HIV_FLT")
+
+unmasker(f"C T R P N [MASK] N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C")
+
+[
+  {
+    "score": 0.9581968188285828,
+    "token": 17,
+    "token_str": "N",
+    "sequence": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
+  },
+  {
+    "score": 0.022986575961112976,
+    "token": 12,
+    "token_str": "K",
+    "sequence": "C T R P N K N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
+  },
+  {
+    "score": 0.003997281193733215,
+    "token": 14,
+    "token_str": "D",
+    "sequence": "C T R P N D N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
+  },
+  {
+    "score": 0.003636382520198822,
+    "token": 15,
+    "token_str": "T",
+    "sequence": "C T R P N T N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
+  },
+  {
+    "score": 0.002701344434171915,
+    "token": 10,
+    "token_str": "S",
+    "sequence": "C T R P N S N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
+  }
+]
+
+```
 
 ## Training Data
 
@@ -53,9 +105,6 @@ As with the [rostlab/Prot-bert-bfd](https://huggingface.co/Rostlab/prot_bert_bfd
 
 Training was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset.
 
-## Evaluation Results
-
-[Table of Prot-Bert and HIV-Bert loss on HIV sequence datasets]
 
 ## BibTeX Entry and Citation Info