Push model using huggingface_hub.

README.md

@@ -24,7 +24,7 @@ You can then generate text as follows:
 ```python
 from transformers import pipeline
 
-generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp_9c08e1c/Evan-Lin/Bart-abs-yelp-allure-2")
+generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpj388pof6/Evan-Lin/Bart-abs-yelp-allure-2")
 outputs = generator("Hello, my llama is cute")
 ```
 
@@ -34,8 +34,8 @@ If you want to use the model for training or to obtain the outputs from the valu
 from transformers import AutoTokenizer
 from trl import AutoModelForCausalLMWithValueHead
 
-tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp_9c08e1c/Evan-Lin/Bart-abs-yelp-allure-2")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp_9c08e1c/Evan-Lin/Bart-abs-yelp-allure-2")
+tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpj388pof6/Evan-Lin/Bart-abs-yelp-allure-2")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpj388pof6/Evan-Lin/Bart-abs-yelp-allure-2")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "Evan-Lin/yelp-abs-bart-base-all",
+  "_name_or_path": "Evan-Lin/yelp-abs-bart-base-30-words",
   "activation_dropout": 0.1,
   "activation_function": "gelu",
   "add_bias_logits": false,

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7299ca0390b27a8e5e6ee81565feed41f0ab23aff9518c5e8ec565a305511a3d
+oid sha256:b4a9cb609d7d9d3dff1b3fba3645ba3af60d1875636e01cc00ec8923131fd400
 size 557974885